无线胶囊内窥镜系统及内窥图像反降晰研究
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摘要
无线胶囊内窥镜可以无创地获得整个消化道的图像,大大拓展了医生的诊断视野。它可以克服传统的推进式内窥镜的缺点,无创伤、无痛苦,通过吞咽进入肠道,可以对食道、胃、小肠和大肠进行特定和非特定位置图像拍摄和分析,实现对整个消化道系统进行检测。开展无线胶囊内窥镜系统及其图像相关方面的研究对于人类社会保健普查工作、消化系症状与体征病人的诊查,具有重要意义和明显的社会与经济效益。
目前,无线胶囊内窥镜系统的研究一般集中在系统的研制方面,较少研究无线胶囊内窥镜在肠道内拍摄图像时出现的模糊现象及其图像复原问题。由于现有技术条件的限制,目前还很难对无线胶囊内窥镜在体内的姿态进行主动控制,因此无法对某部分进行主动对焦或主动停下来拍摄,这往往有可能造成拍摄图像的模糊。从目前的应用来看,还有一个突出问题就是采用纽扣电池供电的胶囊内窥镜工作时间有限,可能会出现还没到达预定的病灶点电池就已经用完的情况。本文的主要目的是在保证满足一定性能的情况下,研究一种能长时间工作、图像质量较高的无线胶囊内窥镜系统。
具体研究内容及主要工作可以概括为以下几个方面:
1.在分析、参考国内外相关无线胶囊内窥镜系统结构的基础上,确定了本系统的总体设计方案;设计了图像传感器驱动系统、无线收发系统、图像色彩空间的转换与显示;设计了无线能量传输模块,并研制了样机系统。
2.分析了消化道中空间不变模糊图像的成像原理及其频域特征。结合内窥图像的特点,探讨了其它一些模糊参数识别方法的局限性,提出了相关系数分析法和倒谱分析法用于内窥图像模糊参数的识别应用。这两种算法都能很好地同时适应散焦模糊和运动模糊的参数识别。研究了自动区分散焦模糊、运动模糊的算法;对这两种模糊的混合模型也提出了一种比较有效的识别方法。
3.分析了消化道中空间变化模糊图像的成像原理,着重研究了空间变化散焦模糊图像的识别与恢复。空间变化模糊图像的恢复至今都是一个难点,一方面很难识别其空间变化的参数、另一方面其图像也很难恢复。本文分析了现有空间变化参数识别方法的局限性,提出了一种交互式的识别与恢复方法。通过对比分析后,选用误差-参数分析法并加以改进后用来识别空间变化模糊图像的模糊参数,最后对空间变化散焦模糊内窥图像做了复原实验。
尽管本文的研究基本实现了研究目标,但整个系统距离实用化还有许多工作需要进行,在本文的最后,对整个论文的工作和研究成果进行了总结,并提出了下一步的研究工作。
A wireless capsule endoscope could capture the images of the entire alimentary tract thus extending the vision of the diagnosing greatly. It could work in the digestive tract non-invasively and lighten the discomfort of patient. Swallowed by patients and then moved forward by a squirm of the gastrointestinal tract, the capsule endoscope can take photos for the entire alimentary tract until it is drained out of the body. Those are the weaknesses of a traditional wire endoscope. It is very important for health care area and the diagnoses of the alimentary tract illnesses to research the wireless endoscope system and its relative image processing technology.
At the present time, primary researches pay more attention to the research on the system structure and the development of the special IC chips for the application rather than the blurred image and their restoration which produced by a variety of reasons during the work process. There are certain prominent phenomena in its clinical application. The cost of a capsule endoscope system containing special IC chips is very high and so the diagnoses cost also is very expensive. Another issue is the blur phenomenon occurs frequently for some endoscope images, but we are not able to control the pose or position of the capsule in the body or control it back to the blurred area to take photos again in the current technological conditions. The more blur the picture, the less the information provided for doctor to diagnose. It would seriously impact the clinical application future of the capsule endoscope. The main goal is to research lower cost and simpler structure wireless capsule endoscope and the restoration of the blurred endoscope image.
The main contents and contributions of this paper are summarized as follows:
1. The evolution and actuality of similar wireless capsule endoscope systems are thoroughly researched. The system overall scheme was designed based on these structures of both home and abroad. The main technical problems in the process of design and realization of this system are analyzed. A prototype capsule endoscope system was made.
2. Analyzing the optical principles of the space-invariant blur image in the alimentary tract and their characters in the frequency domain. Taking account of the characters of the endoscope image, this paper discusses the limitations of some estimation methods for the blur parameters and presents the correlation coefficient algorithm and the cepstrum algorithm to analyze the blurred image. The both methods could adapt well to the defocus and the motion blur simultaneously. An auto-classifying way is presented to delimitate both blur models. Furthermore, the combined blur model by both blur models also could be identified by an effective algorithm.
3. Analyzing the optical principles of the space-variant blur image in the alimentary tract and paying attention to the estimation and restoration issue of the defocus model. It is still a difficult area to research the space-variant blur image includes not only the blur parameters identifying mission but also the restoration. This paper analyzes the limitation of the current popular parameter estimation methods for the space-variant blur image and then presents an interactive algorithm to estimate the space-variant blur parameter and to restore. The Error-Parameter analysis is improved to be applied to the identifying mission. Interactive modifying the auto analysis result according to the experience and the prior information or the information judged by people’s perception. According to the result by the interactive identifying process, a restoration experiment for a space-variant defocused blur endoscope image is introduced.
Although a non-invasive gastrointestinal inspecting system has been developed by the author, there still exists much research work to be done to make the system suitable for clinical applications. At the latter of this paper, a summary for all the research is presented and the future works for the project are introduced.
目前,无线胶囊内窥镜系统的研究一般集中在系统的研制方面,较少研究无线胶囊内窥镜在肠道内拍摄图像时出现的模糊现象及其图像复原问题。由于现有技术条件的限制,目前还很难对无线胶囊内窥镜在体内的姿态进行主动控制,因此无法对某部分进行主动对焦或主动停下来拍摄,这往往有可能造成拍摄图像的模糊。从目前的应用来看,还有一个突出问题就是采用纽扣电池供电的胶囊内窥镜工作时间有限,可能会出现还没到达预定的病灶点电池就已经用完的情况。本文的主要目的是在保证满足一定性能的情况下,研究一种能长时间工作、图像质量较高的无线胶囊内窥镜系统。
具体研究内容及主要工作可以概括为以下几个方面:
1.在分析、参考国内外相关无线胶囊内窥镜系统结构的基础上,确定了本系统的总体设计方案;设计了图像传感器驱动系统、无线收发系统、图像色彩空间的转换与显示;设计了无线能量传输模块,并研制了样机系统。
2.分析了消化道中空间不变模糊图像的成像原理及其频域特征。结合内窥图像的特点,探讨了其它一些模糊参数识别方法的局限性,提出了相关系数分析法和倒谱分析法用于内窥图像模糊参数的识别应用。这两种算法都能很好地同时适应散焦模糊和运动模糊的参数识别。研究了自动区分散焦模糊、运动模糊的算法;对这两种模糊的混合模型也提出了一种比较有效的识别方法。
3.分析了消化道中空间变化模糊图像的成像原理,着重研究了空间变化散焦模糊图像的识别与恢复。空间变化模糊图像的恢复至今都是一个难点,一方面很难识别其空间变化的参数、另一方面其图像也很难恢复。本文分析了现有空间变化参数识别方法的局限性,提出了一种交互式的识别与恢复方法。通过对比分析后,选用误差-参数分析法并加以改进后用来识别空间变化模糊图像的模糊参数,最后对空间变化散焦模糊内窥图像做了复原实验。
尽管本文的研究基本实现了研究目标,但整个系统距离实用化还有许多工作需要进行,在本文的最后,对整个论文的工作和研究成果进行了总结,并提出了下一步的研究工作。
A wireless capsule endoscope could capture the images of the entire alimentary tract thus extending the vision of the diagnosing greatly. It could work in the digestive tract non-invasively and lighten the discomfort of patient. Swallowed by patients and then moved forward by a squirm of the gastrointestinal tract, the capsule endoscope can take photos for the entire alimentary tract until it is drained out of the body. Those are the weaknesses of a traditional wire endoscope. It is very important for health care area and the diagnoses of the alimentary tract illnesses to research the wireless endoscope system and its relative image processing technology.
At the present time, primary researches pay more attention to the research on the system structure and the development of the special IC chips for the application rather than the blurred image and their restoration which produced by a variety of reasons during the work process. There are certain prominent phenomena in its clinical application. The cost of a capsule endoscope system containing special IC chips is very high and so the diagnoses cost also is very expensive. Another issue is the blur phenomenon occurs frequently for some endoscope images, but we are not able to control the pose or position of the capsule in the body or control it back to the blurred area to take photos again in the current technological conditions. The more blur the picture, the less the information provided for doctor to diagnose. It would seriously impact the clinical application future of the capsule endoscope. The main goal is to research lower cost and simpler structure wireless capsule endoscope and the restoration of the blurred endoscope image.
The main contents and contributions of this paper are summarized as follows:
1. The evolution and actuality of similar wireless capsule endoscope systems are thoroughly researched. The system overall scheme was designed based on these structures of both home and abroad. The main technical problems in the process of design and realization of this system are analyzed. A prototype capsule endoscope system was made.
2. Analyzing the optical principles of the space-invariant blur image in the alimentary tract and their characters in the frequency domain. Taking account of the characters of the endoscope image, this paper discusses the limitations of some estimation methods for the blur parameters and presents the correlation coefficient algorithm and the cepstrum algorithm to analyze the blurred image. The both methods could adapt well to the defocus and the motion blur simultaneously. An auto-classifying way is presented to delimitate both blur models. Furthermore, the combined blur model by both blur models also could be identified by an effective algorithm.
3. Analyzing the optical principles of the space-variant blur image in the alimentary tract and paying attention to the estimation and restoration issue of the defocus model. It is still a difficult area to research the space-variant blur image includes not only the blur parameters identifying mission but also the restoration. This paper analyzes the limitation of the current popular parameter estimation methods for the space-variant blur image and then presents an interactive algorithm to estimate the space-variant blur parameter and to restore. The Error-Parameter analysis is improved to be applied to the identifying mission. Interactive modifying the auto analysis result according to the experience and the prior information or the information judged by people’s perception. According to the result by the interactive identifying process, a restoration experiment for a space-variant defocused blur endoscope image is introduced.
Although a non-invasive gastrointestinal inspecting system has been developed by the author, there still exists much research work to be done to make the system suitable for clinical applications. At the latter of this paper, a summary for all the research is presented and the future works for the project are introduced.
引文
[1] S M Greengrass,M. Cunningham, Endoscopy. Measurement+Control, 1993. 26.
[2] 吴云林, 张曙, 褚晔等, M2A 胶囊内镜在消化病诊断中的应用研究. 2003. 20(04): p.230-233.
[3] 梁宝松, 杨玉秀 李修岭, 胶囊内镜在消化道疾病诊断中的应用初探. 2003. 3(10): p.1499-1501.
[4] 关玉霞,陆秋香. 胶囊内窥镜的临床应用及护理.全国内科护理学术交流会议. 2003. 昆明: 中华护理学会.p.245-247
[5] 王桂萍. 介绍胶囊内窥镜的检查及检查前中后的护理.全国内科护理学术交流暨专题讲座会议. 2003. 昆明: 中华护理学会.p.9-10
[6] 张思杰, 彭承琳 郑小林, 基于 MEMS 技术的胃肠道无线内窥镜的研究进展. 2004. 25(5): p.26-27.
[7] 陈孝, 张子其 吴本俨, 胶囊内镜的临床应用. 2002. 21(2).
[8] JD, W., Small-intestinal endoscopy. Endoscopy, 2001(33): p.24-30.
[9] Appleyard M, Fireman Z, Glukhovsky A, et al., A randomized trial comparing wireless capsule endoscopy with push enteroscopy for the detection of small-bowel lesions. Gastroenterology, 2000. 119: p.1431-1438.
[10] Scapa E, Broide E, and A. D, Wireless capsule endoscopy in patients with suspected Crohn's disease and other small bowel abnormalities. Gastroenterology, 2000 120(suppl 1): p.A-40.
[11] Appleyard M, Glukhovsky A, and S. P, Wireless capsule diagnostic endoscopy for recurrent small bowel bleeding. N Engl J Med, 2001 344 p.232-233.
[12] 陈孝, 张子其, 张建萍等, 胶囊内镜对小肠肿瘤的诊断价值. 2004. 25(2): p.142-144.
[13] Iddan G, Meron G, and Glukhovsky A, Wireless capsule endoscopy. Nature, 2000: p.405-417.
[14] 李韪韬 王惠南, 胶囊内窥镜的研究进展. 2005. 20(2): p.4-7.
[15] 张艳辉 黄战华, 胶囊内窥镜技术的研究进展. 2006. 12(4): p.4-7.
[16] 王文兴, 人体胃肠道无创诊察系统及其实验研究. 2004, 上海交通大学博士学位论文: 上海.
[17] Given imaging co. http://www.givenimaging.com/.
[18] RFSystems.Lab. http://www.rfamerica.com/sayaka/index.html.
[19] H.J. Park, H.W. Nam, B.S. Song, et al. Design of bi-directional and multi-channel miniaturized telemetry module for wireless endoscopy 2nd Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine & Biology. 2002. Madison,USA.p.273-276
[20] H.J. Park, H.W. Nam, B.S. Song, et al. Design of bi-directional and multi-channel miniaturized telemetry module for wireless endoscopy.2nd Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine & Biology. 2002. Madison, USA.p.273-276
[21] IMC. http://www.microsystem.re.kr/main_eng/menu04/sub_menu01.asp.
[22] 张锦华, 彭承琳, 赵德春等, 基于 Dsp 的无线胶囊内窥镜视频采集处理系统. 2006. 30(4): p.245-246.
[23] 张思杰, 彭承琳, 郑小林等, 胃肠道无线内窥镜中可吞服摄像药丸的研究 2005. 29(5): p.328-330.
[24] 重庆金山科技集团. http://www.omom.us/.
[25] 张齐联, 年卫东, 王化虹等, OMOM 胶囊内镜临床应用的初步评价. 2005. 22: p.86-89.
[26] 杨建民, 郑卫华, 张骏等, 国产 OMOM 胶囊内镜对小肠疾病的诊断价值. 2006. 26(10): p.697-698.
[27] 王雷, 李宜辉, 达四平等, 国产胶囊内镜 OMOM 临床应用的进一步研究. 2006. 86(6): p.421-423.
[28] Zhihua, W., X. Xiang, Z. Chun, et al. IC design for a digital wireless endoscope capsule system.Radio Science Conference, . 2004.p.K42-K43
[29] 谢翔, 李国林, 池保勇等, 双向、数字化的微型无线内窥镜胶囊内数模混合电路设计. 2005. 45(10).
[30] 陈新凯, 谢翔, 秦豫等, 无线内窥镜专用芯片的 FPGA 验证及相关测试. 2005. 30(09): p.46-49.
[31] 简小云, 梅涛 汪小华, 胶囊内窥镜机器人的外磁场驱动方法. 2005. 27(4): p.367-372.
[32] 付国强, 梅涛 孔德义, 无线微型机器人肠道内窥镜系统中图像采集与无线传输子系统的设计. 2002. 10(6): p.614-618.
[33] 国家知识产权网站. http://www.sipo.gov.cn/sipo/.
[34] 邹谋炎, 反卷积与信号复原. 2001, 北京: 国防工业出版社.
[35] 冈萨雷斯, 数字图像处理(第二版). 2003, 北京: 电子工业出版社.
[36] 阮秋琦, 数字图像处理学. 2001, 北京: 电子工业出版社.
[37] ANDRE JALOBEANU, LAURE BLANC-FERAUD, and JOSIANE ZERUBIA, Satellite Image Deblurring Using Complex Wavelet Packets. International Journal of Computer Vision, 2003. 51(3): p.205-217.
[38] Xiong, Y.,S.A. Shafer. Depth from focusing and defocusing. 1993.p.68-73
[39] Subbarao, M., T.C. Wei, and G. Surya, Focused image recovery from two defocused images recorded with different camera settings. Image Processing, IEEE Transactions on, 1995. 4(12): p.1613-1628.
[40] Deschenes, F.,D. Ziou. Homotopy-based computation of defocus blur and affine transform. 2003.p.I-398-I-404 vol.1
[41] Nayar, S.K., M. Watanabe, and M. Noguchi, Real-time focus rangesensor. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1996. 18(12): p.1186-1198.
[42] Paolo, F., B. Martin, and S. Stefano, Scene and Motion Reconstruction from Defocused and Motion-Blurred Images via Anisotropic Diffusion. Lecture Notes in Computer Science : Computer Vision - ECCV 2004. 2004.257-269.
[43] Gokstorp, M. Computing depth from out-of-focus blur using a local frequency representation. 1994.p.153-158 vol.1
[44] Seong-Ik, J., C. Jaekyung, L. Yongman, et al., A real-time identification method on motion and out-of focus blur for a video camera. Consumer Electronics, IEEE Transactions on, 1994. 40(2): p.145-153.
[45] Ong, E., W. Lin, Z. Lu, et al. A no-reference quality metric for measuring image blur. 2003.p.469-472 vol.1
[46] Nakagaki, R.,A.K. Katsaggelos. A VQ-based blur identification algorithm. 2003.p.III-725-8 vol.3
[47] Stern, A.,N.S. Kopeika. Analytical method to calculate optical transfer functions for image motion and its implementation in vibrated image restoration. 1996.p.379-382
[48] Panchapakesan, K., D.G. Sheppard, M.W. Marcellin, et al., Blur identification from vector quantizer encoder distortion. Image Processing, IEEE Transactions on, 2001. 10(3): p.465-470.
[49] Ji Soo, L., J. Shah, M.E. Jernigan, et al., Characterization and deblurring of lateral crosstalk in CMOS image sensors. Electron Devices, IEEE Transactions on, 2003. 50(12): p.2361-2368.
[50] Miller, J.P., T. Roska, T. Sziranyi, et al. Deblurring of images by cellular neural networks with applications to microscopy. 1994.p.237-242
[51] Snyder, D.L., T.J. Schulz, and J.A. O'Sullivan, Deblurring subject to nonnegativity constraints. Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on], 1992. 40(5): p.1143-1150.
[52] Romeny, B.M.t.H., L.M.J. Florack, M. de Swart, et al. Deblurring Gaussian blur.Mathematical Methods in Medical Imaging III. 1994. San Diego, CA, USA: SPIE.p.139-148
[53] Rooms, F. Estimating image blur in the wavelet domain. 2002.p.IV-4190 vol.4
[54] Zitova, B.,J. Flusser. Estimation of camera planar motion from blurred images. 2002.p.II-329-II-332 vol.2
[55] Yitzhaky, Y.,N.S. Kopeika. Identification of motion blur for blind image restoration. 1995.p.3.4.3/5
[56] Shekarforoush, H.,R. Chellappa. Blind estimation of PSF for out of focus video data. 1998.p.742-745 vol.3
[57] Hanghang, T., L. Mingjing, Z. Hongjiang, et al. Blur detection fordigital images using wavelet transform. 2004.p.17-20 Vol.1
[58] Price, J.R., T.F. Gee, and K.W. Tobin, Jr. Blur estimation in limited-control environments. 2001.p.1669-1672 vol.3
[59] Savakis, A.E.,J.R.L. Easton. Blur identification based on higher order spectral nulls.Image Reconstruction and Restoration. 1994. San Diego, CA, USA: SPIE.p.168-177
[60] Savakis, A.E.,H.J. Trussell, Blur identification by residual spectral matching. Image Processing, IEEE Transactions on, 1993. 2(2): p.141-151.
[61] Devcic, Z.,S. Loncaric. Blur identification using averaged spectra of degraded image singular vectors. 2000.p.2195-2198 vol.4
[62] Li, C.,Y. Kim-Hui, Efficient discrete spatial techniques for blur support identification in blind image deconvolution. Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on], 2006. 54(4): p.1557-1562.
[63] Sang Ku, K., P. Sang Rae, and P. Joon Ki, Simultaneous out-of-focus blur estimation and restoration for digital auto-focusing system. Consumer Electronics, IEEE Transactions on, 1998. 44(3): p.1071-1075.
[64] Yun-Chung, C., W. Jung-Ming, R.R. Bailey, et al. A non-parametric blur measure based on edge analysis for image processing applications. 2004.p.356-360 vol.1
[65] 傅晓薇 方康玲, 一种改进的快速模糊边缘检测算法. 2002. 24(2): p.128-130.
[66] You, X.,G. Crebbin. Image blur identification by using higher order statistic techniques. 1996.p.77-80 vol.3
[67] Yap, P.T.,P. Raveendran, Image focus measure based on Chebyshev moments. Vision, Image and Signal Processing, IEE Proceedings-, 2004. 151(2): p.128-136.
[68] Premaratne, P.,C.C. Ko, Image blur recognition using under-sampled discrete Fourier transform. Electronics Letters, 1999. 35(11): p.889-890.
[69] Sezan, M.I., G. Pavlovic, A.M. Tekalp, et al. On modeling the focus blur in image restoration. 1991.p.2485-2488 vol.4
[70] Premaratne, P.,M. Premaratne. Accelerated iterative blind deconvolution of still images. 2003.p.6-10 Vol.1
[71] Lagendijk, R.L., J. Biemond, and D.E. Boekee, Regularized iterative image restoration with ringing reduction. Acoustics, Speech, and Signal Processing [see also IEEE Transactions on Signal Processing], IEEE Transactions on, 1988. 36(12): p.1874-1888.
[72] Zhu Ren, F.,H. Zhou. The fixed-phase iterative algorithm recovery of blurred image. 2000.p.898-900 vol.2
[73] Namazi, N.M.,C.M. Fan. A new iterative algorithm for imagerestoration based on maximum likelihood principle. 1992.p.2465-2468 vol.5
[74] Bhaskar, R., J. Hite, and D.E. Pitts. An iterative frequency-domain technique to reduce image degradation caused by lens defocus and linear motion blur. 1994.p.2522-2524 vol.4
[75] Reeves, S.J., Optimal space-varying regularization in iterative image restoration. Image Processing, IEEE Transactions on, 1994. 3(3): p.319-324.
[76] Schafer, R.W., R.M. Mersereau, and M.A. Richards, Constrained iterative restoration algorithms. Proceedings of the IEEE, 1981. 69(4): p.432-450.
[77] Biemond, J., R.L. Lagendijk, and R.M. Mersereau, Iterative methods for image deblurring. Proceedings of the IEEE, 1990. 78(5): p.856-883.
[78] Biggs, D.S.C.,M. Andrews, Acceleration of iterative image restoration algorithms. Applied Optics, 1997. 36(8): p.1766-1775.
[79] Lucy, L.B., An iterative technique for the rectification of observed distributions. The Astronomical Journal, 1974. 79(6): p.745-754.
[80] Zaccheo, T.S.,R.A. Gonsalves, Iterative maximum-likelihood estimators for positively constrained objects. J. Opt. Soc. Am, 1996. 13(2): p.236–242.
[81] Richardson, W.H., Bayesian-based iterative method of image restoration. J. Opt. Soc. Am, 1972. 62(1): p.55-59.
[82] Rajagopalan, A.N.,S. Chaudhuri. Identification of shift-variant point spread function for a class of imaging systems. 1997.p.275-278
[83] Trussell, H.J.,S. Fogel, Identification and restoration of spatially variant motion blurs in sequential images. Image Processing, IEEE Transactions on, 1992. 1(1): p.123-126.
[84] Ozkan, M.K., A.M. Tekalp, and M.I. Sezan. Identification of a class of space-variant image blurs.Image Processing Algorithms and Techniques II. 1991. San Jose, CA, USA: SPIE.p.146-156
[85] Ozkan, M.K., A.M. Tekalp, and M.I. Sezan, POCS-based restoration of space-varying blurred images. Image Processing, IEEE Transactions on, 1994. 3(4): p.450-454.
[86] Costello, T.P.,W.B. Mikhael, One-dimensional comparison of Wiener filtering and Richardson-Lucy methods for sectioned restoration of space-variant digital images. Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on [see also Circuits and Systems I: Regular Papers, IEEE Transactions on], 2002. 49(4): p.518-522.
[87] 王保华,罗立民, 生物医学电子学高级教程. 2001, 南京: 东南大学出版社.
[88] A. Tayfun, Z. Babak, and N. Khalil, RF telemetry powering and controlof hermetically sealed integrated sensors and actuators, in 1990 IEEE Solid-State Sensor and Actuator Workshop. 1990: Hilton Head Island, SC, USA. p. 145-148.
[89] R.T. Philip, A.K.S.M., Closed-Loop Calss E Transcutaneous Power and Data Link for MicroImplants. IEEE Transactions on Biomedical Engineering, 1992. 39(6): p.589-599.
[90] A. Ghahary,B.H. Cho., Design of a transcutaneous energy transmission system using a series resonant converter. IEEE Transactions on Power Electronics, 1992. 7(2): p.261-269.
[91] A. Tayfun,N. Khalil, A telemetrically powered and controlled implantable neural recording system with CMOS interface circuitry, in Proceedings of the 7th Mediterranean Electrotechnical Conference. 1994: Antalya,Turkey. p. 545-548.
[92] T.H. Nishimura, T.E., K. Hirachi, Y. Maejima, K. Kuwana, M. Saito, A large air gap flat transformer for a transcutaneous energy transmission system, in 25th Annual IEEE Power Electronics Specialists Conference. 1994: Taipei, Taiwan. p. 1323-1329.
[93] C.G. Kim,B.H. Cho, Transcutaneous energy transmission with double tuned duty cycle control, in Proceedings of the 31st Intersociety Energy Conversion Engineering Conference. 1996: Washington, DC, USA. p. 587-591.
[94] C.C. Tsai, B.S. Chen, and C.M. Tsai, Design of wireless transcutaneous energy transmission system for totally artificial hearts, in The 2000 IEEE Asia-Pacific Conference on Circuits and Systems. 2000: Tianjin, China. p. 646-649.
[95] B.J Gyu,B.H. Cho, An Energy Transmission System for an Artificial Heart Using Leakage Inductance Compensation of Tanscutaneous Transformer. IEEE Transactions on Power Electronics, 1998. 13(6): p.1013-1022.
[96] A. Djemouai, M. Sawan, and M. Slamani, An Efficient RF power transfer and bidirectional data transmission to implantable electronic devices, in Proceedings of the 1999 IEEE International Symposium on Circuits and Systems. 1999: Orlando, FL, USA. p. 259-262.
[97] G. Gudnason, J.H. Nielsen, and E. Bruun, A distributed transducer system for functional electrical stimulation, in The 8th IEEE International Conference on Electronics Circuit and System. 2001: Malta. p. 397-400.
[98] Semiconductors, P., THE I2C-BUS SPECIFICATION. 2000.
[99] Siliconimaging. http://www.siliconimaging.com/RGB%20Bayer.htm.
[100] fourcc. http://www.fourcc.org/yuv.php.
[101]Microsoft.www.microsoft.com/china/MSDN/library/enterprisedevelopment/softwaredev/VideoRende8BitYUV.mspx.
[102] F. Sato, T. Nomoto, and G. Kano, A new contactless power-signal transmission device for implanted functional electricalstimulation (FES) Magnetics, IEEE Transactions on, 2004. 40(4): p.2964-2966.
[103] Ali Ghahary,Bo H. Cho, Design of a transcutaneous energy transmission system using a serial resonant converter. IEEE Trans.Power Electrics, 1992. 7(2).
[104] H. Matsuki,M. Shiiki, Investigation of coil geometry for transcutaneous energy tranmission for artificial heart. IEEE Trans.Magnetics, 1992. 28(5).
[105] 赵凯华,陈熙谋, 电磁学. 2003, 北京: 高等教育出版社.
[106] 胡广书, 数字信号处理——理论、算法与实现. 2003, 北京: 清华大学出版社.
[107] 黄世霖, 工程信号处理. 1986, 北京: 人民交通出版社.
[108] 张志佳, 黄莎白 史泽林, 新的基于边缘特征的图像相关匹配方法. 2003. 32(6): p.635-638.
[109] 王博, 频域相关的恒常性特征及其算法研究. 中国图象图形学报, 2006. 11(6): p.798-803.
[110] 姚天任, 现代数字信号处理. 2004, 武汉: 华中科技大学出版社.
[111] Bhaskar, R., J. Hite, D.E. Pitts, et al., An iterative frequency-domain technique to reduce image degradationcaused by lens defocus and linear motion blur, in Geoscience and Remote Sensing Symposium. 1994: Pasadena, CA, USA. p. 2522-2524
[112] Chang, M.M., A.M. Tekalp, and A.T. Erdem, Blur identification using the bispectrum. Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on], 1991. 39(10): p.2323-2325.
[113] Rom, R., On the cepstrum of two-dimensional functions (Corresp.). Information Theory, IEEE Transactions on, 1975. 21(2): p.214-217.
[114] 徐守时, 信号与系统. 1999, 合肥: 中国科学技术大学出版社.
[115] Gross, F.B., New approximations to J0 and J1 Bessel functions. Antennas and Propagation, IEEE Transactions on, 1995. 43(8): p.904-907.
[116] 姚端正,梁家宝, 数学物理方法. 1992, 武汉: 武汉大学出版社
[117] 阮秋琦, 数字图像处理学. 2004, 北京: 电子工业出版社.
[118] Anony, 数字图像恢复和重建. 1980.
[119] 葛显良, 应用泛函分析. 1996, 杭州: 浙江大学出版社.
[120] Cannon, M., Blind deconvolution of spatially invariant image blurs with phase. Acoustics, Speech, and Signal Processing [see also IEEE Transactions on Signal Processing], IEEE Transactions on, 1976. 24(1): p.58-63.
[121] Tekalp, A.M., H. Kaufman, and J.W. Woods. Decision-directed segmentation for the restoration of images degraded by a class of space-variant blurs. 1988.p.980-983 vol.2
[122] Martin, W., T. David, and W. Joachim, Variational Deblurring of Images with Uncertain and Spatially Variant Blurs. Lecture Notesin Computer Science : Pattern Recognition. 2005.485-492.
[123] Patti, A.J., M.K. Ozkan, A.M. Tekalp, et al. New approaches for space-invariant image restoration. 1993.p.261-264 vol.5
[124] Tekalp, A.M.,C. Pavlovic. Space-variant and color image restoration using Kalman filtering. 1989.p.9-12 vol.1
[125] Costello, T.P.,W.B. Mikhael. Restoration of digital images with known space-variant blurs from conventional optical systems.Visual Information Processing VIII. 1999. Orlando, FL, USA: SPIE.p.71-79
[126] Costello, T.P.,W.B. Mikhael, Efficient restoration of space-variant blurs from physical optics by sectioning with modified Wiener filtering. Digital Signal Processing, 2003. 13: p.1-22.
[127] Trussell, H.,M. Civanlar, The feasible solution in signal restoration. Acoustics, Speech, and Signal Processing [see also IEEE Transactions on Signal Processing], IEEE Transactions on, 1984. 32(2): p.201-212.
[128] 冈萨雷斯, 数字图像处理(MATLAB 版). 2005.473.
[129] 孔祥龙, 李玉同, 远晓辉等, Lucy-Richardson 算法用于针孔图像的恢复. 2006. 55(5): p.2364-2370.
[130] 王鸿南, 钟文, 汪静等, 图像清晰度评价方法研究. 2004. 9(7): p.828-831.
[131] 朱孔凤, 姜威, 王端芳等, 一种新的图像清晰度评价函数. 2005. 34(4): p.464-468.
[132] 曹茂永, 孙农亮 郁道银, 离焦模糊图像清晰度评价函数的研究. 2001. 22(3): p.259-268.
[133] 袁珂 徐蔚鸿, 基于图像清晰度评价的摄像头辅助调焦系统. 2006. 33(1): p.141-144.
[134] Xin, L. Blind image quality assessment.Image Processing. 2002. Proceedings. 2002 International Conference on. 2002.p.I-449-I-452 vol.1
[135] Eskicioglu, A.M.,P.S. Fisher, Image quality measures and their performance. Communications, IEEE Transactions on, 1995. 43(12): p.2959-2965.
[136] Damera-Venkata, N., T.D. Kite, W.S. Geisler, et al., Image quality assessment based on a degradation model. Image Processing, IEEE Transactions on, 2000. 9(4): p.636-650.
[137] 汪孔桥, 数字图像的质量评价. 2000. 19(5): p.14-16.
[138] 刘晶晶, 晏磊, 赵红颖等, 数字图像运动模糊复原质量评价方法研究. 2007(1): p.13-15.
[139] 杨守义 罗伟雄, 一种基于高阶统计量的图像质量客观评价方法. 2001. 21(5): p.610-613.
[140] 丁绪星, 朱日宏 李建欣, 一种基于人眼视觉特性的图像质量评价. 2004. 9(2): p.190-194.
[141] 魏政刚, 袁杰辉 蔡元龙, 一种基于视觉感知的图像质量评价方法. 1999.27(4): p.79-82.
[142] 周 建 鹏 杨 义 先 , 一 种 图象 质量 的 感 知 测 量 方法 . 1998. 3(3): p.200-204.
[143] Hafner, J., H.S. Sawhney, W. Equitz, et al., Efficient color histogram indexing for quadratic form distance functions. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1995. 17(7): p.729-736.
[144] Suryani, L.,L. Guojun. Spatial statistics for content based image retrieval. 2003.p.155-159
[145] 飞思科技产品研发中心, MATLAB6.5 辅助小波分析与应用. 2003, 北京: 电子工业出版社.283.
[146] 周贤, 姜威 朱孔凤, 基于小波多分辨率分析的自动聚焦算法. 2006. 23(2): p.104-134.
[2] 吴云林, 张曙, 褚晔等, M2A 胶囊内镜在消化病诊断中的应用研究. 2003. 20(04): p.230-233.
[3] 梁宝松, 杨玉秀 李修岭, 胶囊内镜在消化道疾病诊断中的应用初探. 2003. 3(10): p.1499-1501.
[4] 关玉霞,陆秋香. 胶囊内窥镜的临床应用及护理.全国内科护理学术交流会议. 2003. 昆明: 中华护理学会.p.245-247
[5] 王桂萍. 介绍胶囊内窥镜的检查及检查前中后的护理.全国内科护理学术交流暨专题讲座会议. 2003. 昆明: 中华护理学会.p.9-10
[6] 张思杰, 彭承琳 郑小林, 基于 MEMS 技术的胃肠道无线内窥镜的研究进展. 2004. 25(5): p.26-27.
[7] 陈孝, 张子其 吴本俨, 胶囊内镜的临床应用. 2002. 21(2).
[8] JD, W., Small-intestinal endoscopy. Endoscopy, 2001(33): p.24-30.
[9] Appleyard M, Fireman Z, Glukhovsky A, et al., A randomized trial comparing wireless capsule endoscopy with push enteroscopy for the detection of small-bowel lesions. Gastroenterology, 2000. 119: p.1431-1438.
[10] Scapa E, Broide E, and A. D, Wireless capsule endoscopy in patients with suspected Crohn's disease and other small bowel abnormalities. Gastroenterology, 2000 120(suppl 1): p.A-40.
[11] Appleyard M, Glukhovsky A, and S. P, Wireless capsule diagnostic endoscopy for recurrent small bowel bleeding. N Engl J Med, 2001 344 p.232-233.
[12] 陈孝, 张子其, 张建萍等, 胶囊内镜对小肠肿瘤的诊断价值. 2004. 25(2): p.142-144.
[13] Iddan G, Meron G, and Glukhovsky A, Wireless capsule endoscopy. Nature, 2000: p.405-417.
[14] 李韪韬 王惠南, 胶囊内窥镜的研究进展. 2005. 20(2): p.4-7.
[15] 张艳辉 黄战华, 胶囊内窥镜技术的研究进展. 2006. 12(4): p.4-7.
[16] 王文兴, 人体胃肠道无创诊察系统及其实验研究. 2004, 上海交通大学博士学位论文: 上海.
[17] Given imaging co. http://www.givenimaging.com/.
[18] RFSystems.Lab. http://www.rfamerica.com/sayaka/index.html.
[19] H.J. Park, H.W. Nam, B.S. Song, et al. Design of bi-directional and multi-channel miniaturized telemetry module for wireless endoscopy 2nd Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine & Biology. 2002. Madison,USA.p.273-276
[20] H.J. Park, H.W. Nam, B.S. Song, et al. Design of bi-directional and multi-channel miniaturized telemetry module for wireless endoscopy.2nd Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine & Biology. 2002. Madison, USA.p.273-276
[21] IMC. http://www.microsystem.re.kr/main_eng/menu04/sub_menu01.asp.
[22] 张锦华, 彭承琳, 赵德春等, 基于 Dsp 的无线胶囊内窥镜视频采集处理系统. 2006. 30(4): p.245-246.
[23] 张思杰, 彭承琳, 郑小林等, 胃肠道无线内窥镜中可吞服摄像药丸的研究 2005. 29(5): p.328-330.
[24] 重庆金山科技集团. http://www.omom.us/.
[25] 张齐联, 年卫东, 王化虹等, OMOM 胶囊内镜临床应用的初步评价. 2005. 22: p.86-89.
[26] 杨建民, 郑卫华, 张骏等, 国产 OMOM 胶囊内镜对小肠疾病的诊断价值. 2006. 26(10): p.697-698.
[27] 王雷, 李宜辉, 达四平等, 国产胶囊内镜 OMOM 临床应用的进一步研究. 2006. 86(6): p.421-423.
[28] Zhihua, W., X. Xiang, Z. Chun, et al. IC design for a digital wireless endoscope capsule system.Radio Science Conference, . 2004.p.K42-K43
[29] 谢翔, 李国林, 池保勇等, 双向、数字化的微型无线内窥镜胶囊内数模混合电路设计. 2005. 45(10).
[30] 陈新凯, 谢翔, 秦豫等, 无线内窥镜专用芯片的 FPGA 验证及相关测试. 2005. 30(09): p.46-49.
[31] 简小云, 梅涛 汪小华, 胶囊内窥镜机器人的外磁场驱动方法. 2005. 27(4): p.367-372.
[32] 付国强, 梅涛 孔德义, 无线微型机器人肠道内窥镜系统中图像采集与无线传输子系统的设计. 2002. 10(6): p.614-618.
[33] 国家知识产权网站. http://www.sipo.gov.cn/sipo/.
[34] 邹谋炎, 反卷积与信号复原. 2001, 北京: 国防工业出版社.
[35] 冈萨雷斯, 数字图像处理(第二版). 2003, 北京: 电子工业出版社.
[36] 阮秋琦, 数字图像处理学. 2001, 北京: 电子工业出版社.
[37] ANDRE JALOBEANU, LAURE BLANC-FERAUD, and JOSIANE ZERUBIA, Satellite Image Deblurring Using Complex Wavelet Packets. International Journal of Computer Vision, 2003. 51(3): p.205-217.
[38] Xiong, Y.,S.A. Shafer. Depth from focusing and defocusing. 1993.p.68-73
[39] Subbarao, M., T.C. Wei, and G. Surya, Focused image recovery from two defocused images recorded with different camera settings. Image Processing, IEEE Transactions on, 1995. 4(12): p.1613-1628.
[40] Deschenes, F.,D. Ziou. Homotopy-based computation of defocus blur and affine transform. 2003.p.I-398-I-404 vol.1
[41] Nayar, S.K., M. Watanabe, and M. Noguchi, Real-time focus rangesensor. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1996. 18(12): p.1186-1198.
[42] Paolo, F., B. Martin, and S. Stefano, Scene and Motion Reconstruction from Defocused and Motion-Blurred Images via Anisotropic Diffusion. Lecture Notes in Computer Science : Computer Vision - ECCV 2004. 2004.257-269.
[43] Gokstorp, M. Computing depth from out-of-focus blur using a local frequency representation. 1994.p.153-158 vol.1
[44] Seong-Ik, J., C. Jaekyung, L. Yongman, et al., A real-time identification method on motion and out-of focus blur for a video camera. Consumer Electronics, IEEE Transactions on, 1994. 40(2): p.145-153.
[45] Ong, E., W. Lin, Z. Lu, et al. A no-reference quality metric for measuring image blur. 2003.p.469-472 vol.1
[46] Nakagaki, R.,A.K. Katsaggelos. A VQ-based blur identification algorithm. 2003.p.III-725-8 vol.3
[47] Stern, A.,N.S. Kopeika. Analytical method to calculate optical transfer functions for image motion and its implementation in vibrated image restoration. 1996.p.379-382
[48] Panchapakesan, K., D.G. Sheppard, M.W. Marcellin, et al., Blur identification from vector quantizer encoder distortion. Image Processing, IEEE Transactions on, 2001. 10(3): p.465-470.
[49] Ji Soo, L., J. Shah, M.E. Jernigan, et al., Characterization and deblurring of lateral crosstalk in CMOS image sensors. Electron Devices, IEEE Transactions on, 2003. 50(12): p.2361-2368.
[50] Miller, J.P., T. Roska, T. Sziranyi, et al. Deblurring of images by cellular neural networks with applications to microscopy. 1994.p.237-242
[51] Snyder, D.L., T.J. Schulz, and J.A. O'Sullivan, Deblurring subject to nonnegativity constraints. Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on], 1992. 40(5): p.1143-1150.
[52] Romeny, B.M.t.H., L.M.J. Florack, M. de Swart, et al. Deblurring Gaussian blur.Mathematical Methods in Medical Imaging III. 1994. San Diego, CA, USA: SPIE.p.139-148
[53] Rooms, F. Estimating image blur in the wavelet domain. 2002.p.IV-4190 vol.4
[54] Zitova, B.,J. Flusser. Estimation of camera planar motion from blurred images. 2002.p.II-329-II-332 vol.2
[55] Yitzhaky, Y.,N.S. Kopeika. Identification of motion blur for blind image restoration. 1995.p.3.4.3/5
[56] Shekarforoush, H.,R. Chellappa. Blind estimation of PSF for out of focus video data. 1998.p.742-745 vol.3
[57] Hanghang, T., L. Mingjing, Z. Hongjiang, et al. Blur detection fordigital images using wavelet transform. 2004.p.17-20 Vol.1
[58] Price, J.R., T.F. Gee, and K.W. Tobin, Jr. Blur estimation in limited-control environments. 2001.p.1669-1672 vol.3
[59] Savakis, A.E.,J.R.L. Easton. Blur identification based on higher order spectral nulls.Image Reconstruction and Restoration. 1994. San Diego, CA, USA: SPIE.p.168-177
[60] Savakis, A.E.,H.J. Trussell, Blur identification by residual spectral matching. Image Processing, IEEE Transactions on, 1993. 2(2): p.141-151.
[61] Devcic, Z.,S. Loncaric. Blur identification using averaged spectra of degraded image singular vectors. 2000.p.2195-2198 vol.4
[62] Li, C.,Y. Kim-Hui, Efficient discrete spatial techniques for blur support identification in blind image deconvolution. Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on], 2006. 54(4): p.1557-1562.
[63] Sang Ku, K., P. Sang Rae, and P. Joon Ki, Simultaneous out-of-focus blur estimation and restoration for digital auto-focusing system. Consumer Electronics, IEEE Transactions on, 1998. 44(3): p.1071-1075.
[64] Yun-Chung, C., W. Jung-Ming, R.R. Bailey, et al. A non-parametric blur measure based on edge analysis for image processing applications. 2004.p.356-360 vol.1
[65] 傅晓薇 方康玲, 一种改进的快速模糊边缘检测算法. 2002. 24(2): p.128-130.
[66] You, X.,G. Crebbin. Image blur identification by using higher order statistic techniques. 1996.p.77-80 vol.3
[67] Yap, P.T.,P. Raveendran, Image focus measure based on Chebyshev moments. Vision, Image and Signal Processing, IEE Proceedings-, 2004. 151(2): p.128-136.
[68] Premaratne, P.,C.C. Ko, Image blur recognition using under-sampled discrete Fourier transform. Electronics Letters, 1999. 35(11): p.889-890.
[69] Sezan, M.I., G. Pavlovic, A.M. Tekalp, et al. On modeling the focus blur in image restoration. 1991.p.2485-2488 vol.4
[70] Premaratne, P.,M. Premaratne. Accelerated iterative blind deconvolution of still images. 2003.p.6-10 Vol.1
[71] Lagendijk, R.L., J. Biemond, and D.E. Boekee, Regularized iterative image restoration with ringing reduction. Acoustics, Speech, and Signal Processing [see also IEEE Transactions on Signal Processing], IEEE Transactions on, 1988. 36(12): p.1874-1888.
[72] Zhu Ren, F.,H. Zhou. The fixed-phase iterative algorithm recovery of blurred image. 2000.p.898-900 vol.2
[73] Namazi, N.M.,C.M. Fan. A new iterative algorithm for imagerestoration based on maximum likelihood principle. 1992.p.2465-2468 vol.5
[74] Bhaskar, R., J. Hite, and D.E. Pitts. An iterative frequency-domain technique to reduce image degradation caused by lens defocus and linear motion blur. 1994.p.2522-2524 vol.4
[75] Reeves, S.J., Optimal space-varying regularization in iterative image restoration. Image Processing, IEEE Transactions on, 1994. 3(3): p.319-324.
[76] Schafer, R.W., R.M. Mersereau, and M.A. Richards, Constrained iterative restoration algorithms. Proceedings of the IEEE, 1981. 69(4): p.432-450.
[77] Biemond, J., R.L. Lagendijk, and R.M. Mersereau, Iterative methods for image deblurring. Proceedings of the IEEE, 1990. 78(5): p.856-883.
[78] Biggs, D.S.C.,M. Andrews, Acceleration of iterative image restoration algorithms. Applied Optics, 1997. 36(8): p.1766-1775.
[79] Lucy, L.B., An iterative technique for the rectification of observed distributions. The Astronomical Journal, 1974. 79(6): p.745-754.
[80] Zaccheo, T.S.,R.A. Gonsalves, Iterative maximum-likelihood estimators for positively constrained objects. J. Opt. Soc. Am, 1996. 13(2): p.236–242.
[81] Richardson, W.H., Bayesian-based iterative method of image restoration. J. Opt. Soc. Am, 1972. 62(1): p.55-59.
[82] Rajagopalan, A.N.,S. Chaudhuri. Identification of shift-variant point spread function for a class of imaging systems. 1997.p.275-278
[83] Trussell, H.J.,S. Fogel, Identification and restoration of spatially variant motion blurs in sequential images. Image Processing, IEEE Transactions on, 1992. 1(1): p.123-126.
[84] Ozkan, M.K., A.M. Tekalp, and M.I. Sezan. Identification of a class of space-variant image blurs.Image Processing Algorithms and Techniques II. 1991. San Jose, CA, USA: SPIE.p.146-156
[85] Ozkan, M.K., A.M. Tekalp, and M.I. Sezan, POCS-based restoration of space-varying blurred images. Image Processing, IEEE Transactions on, 1994. 3(4): p.450-454.
[86] Costello, T.P.,W.B. Mikhael, One-dimensional comparison of Wiener filtering and Richardson-Lucy methods for sectioned restoration of space-variant digital images. Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on [see also Circuits and Systems I: Regular Papers, IEEE Transactions on], 2002. 49(4): p.518-522.
[87] 王保华,罗立民, 生物医学电子学高级教程. 2001, 南京: 东南大学出版社.
[88] A. Tayfun, Z. Babak, and N. Khalil, RF telemetry powering and controlof hermetically sealed integrated sensors and actuators, in 1990 IEEE Solid-State Sensor and Actuator Workshop. 1990: Hilton Head Island, SC, USA. p. 145-148.
[89] R.T. Philip, A.K.S.M., Closed-Loop Calss E Transcutaneous Power and Data Link for MicroImplants. IEEE Transactions on Biomedical Engineering, 1992. 39(6): p.589-599.
[90] A. Ghahary,B.H. Cho., Design of a transcutaneous energy transmission system using a series resonant converter. IEEE Transactions on Power Electronics, 1992. 7(2): p.261-269.
[91] A. Tayfun,N. Khalil, A telemetrically powered and controlled implantable neural recording system with CMOS interface circuitry, in Proceedings of the 7th Mediterranean Electrotechnical Conference. 1994: Antalya,Turkey. p. 545-548.
[92] T.H. Nishimura, T.E., K. Hirachi, Y. Maejima, K. Kuwana, M. Saito, A large air gap flat transformer for a transcutaneous energy transmission system, in 25th Annual IEEE Power Electronics Specialists Conference. 1994: Taipei, Taiwan. p. 1323-1329.
[93] C.G. Kim,B.H. Cho, Transcutaneous energy transmission with double tuned duty cycle control, in Proceedings of the 31st Intersociety Energy Conversion Engineering Conference. 1996: Washington, DC, USA. p. 587-591.
[94] C.C. Tsai, B.S. Chen, and C.M. Tsai, Design of wireless transcutaneous energy transmission system for totally artificial hearts, in The 2000 IEEE Asia-Pacific Conference on Circuits and Systems. 2000: Tianjin, China. p. 646-649.
[95] B.J Gyu,B.H. Cho, An Energy Transmission System for an Artificial Heart Using Leakage Inductance Compensation of Tanscutaneous Transformer. IEEE Transactions on Power Electronics, 1998. 13(6): p.1013-1022.
[96] A. Djemouai, M. Sawan, and M. Slamani, An Efficient RF power transfer and bidirectional data transmission to implantable electronic devices, in Proceedings of the 1999 IEEE International Symposium on Circuits and Systems. 1999: Orlando, FL, USA. p. 259-262.
[97] G. Gudnason, J.H. Nielsen, and E. Bruun, A distributed transducer system for functional electrical stimulation, in The 8th IEEE International Conference on Electronics Circuit and System. 2001: Malta. p. 397-400.
[98] Semiconductors, P., THE I2C-BUS SPECIFICATION. 2000.
[99] Siliconimaging. http://www.siliconimaging.com/RGB%20Bayer.htm.
[100] fourcc. http://www.fourcc.org/yuv.php.
[101]Microsoft.www.microsoft.com/china/MSDN/library/enterprisedevelopment/softwaredev/VideoRende8BitYUV.mspx.
[102] F. Sato, T. Nomoto, and G. Kano, A new contactless power-signal transmission device for implanted functional electricalstimulation (FES) Magnetics, IEEE Transactions on, 2004. 40(4): p.2964-2966.
[103] Ali Ghahary,Bo H. Cho, Design of a transcutaneous energy transmission system using a serial resonant converter. IEEE Trans.Power Electrics, 1992. 7(2).
[104] H. Matsuki,M. Shiiki, Investigation of coil geometry for transcutaneous energy tranmission for artificial heart. IEEE Trans.Magnetics, 1992. 28(5).
[105] 赵凯华,陈熙谋, 电磁学. 2003, 北京: 高等教育出版社.
[106] 胡广书, 数字信号处理——理论、算法与实现. 2003, 北京: 清华大学出版社.
[107] 黄世霖, 工程信号处理. 1986, 北京: 人民交通出版社.
[108] 张志佳, 黄莎白 史泽林, 新的基于边缘特征的图像相关匹配方法. 2003. 32(6): p.635-638.
[109] 王博, 频域相关的恒常性特征及其算法研究. 中国图象图形学报, 2006. 11(6): p.798-803.
[110] 姚天任, 现代数字信号处理. 2004, 武汉: 华中科技大学出版社.
[111] Bhaskar, R., J. Hite, D.E. Pitts, et al., An iterative frequency-domain technique to reduce image degradationcaused by lens defocus and linear motion blur, in Geoscience and Remote Sensing Symposium. 1994: Pasadena, CA, USA. p. 2522-2524
[112] Chang, M.M., A.M. Tekalp, and A.T. Erdem, Blur identification using the bispectrum. Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on], 1991. 39(10): p.2323-2325.
[113] Rom, R., On the cepstrum of two-dimensional functions (Corresp.). Information Theory, IEEE Transactions on, 1975. 21(2): p.214-217.
[114] 徐守时, 信号与系统. 1999, 合肥: 中国科学技术大学出版社.
[115] Gross, F.B., New approximations to J0 and J1 Bessel functions. Antennas and Propagation, IEEE Transactions on, 1995. 43(8): p.904-907.
[116] 姚端正,梁家宝, 数学物理方法. 1992, 武汉: 武汉大学出版社
[117] 阮秋琦, 数字图像处理学. 2004, 北京: 电子工业出版社.
[118] Anony, 数字图像恢复和重建. 1980.
[119] 葛显良, 应用泛函分析. 1996, 杭州: 浙江大学出版社.
[120] Cannon, M., Blind deconvolution of spatially invariant image blurs with phase. Acoustics, Speech, and Signal Processing [see also IEEE Transactions on Signal Processing], IEEE Transactions on, 1976. 24(1): p.58-63.
[121] Tekalp, A.M., H. Kaufman, and J.W. Woods. Decision-directed segmentation for the restoration of images degraded by a class of space-variant blurs. 1988.p.980-983 vol.2
[122] Martin, W., T. David, and W. Joachim, Variational Deblurring of Images with Uncertain and Spatially Variant Blurs. Lecture Notesin Computer Science : Pattern Recognition. 2005.485-492.
[123] Patti, A.J., M.K. Ozkan, A.M. Tekalp, et al. New approaches for space-invariant image restoration. 1993.p.261-264 vol.5
[124] Tekalp, A.M.,C. Pavlovic. Space-variant and color image restoration using Kalman filtering. 1989.p.9-12 vol.1
[125] Costello, T.P.,W.B. Mikhael. Restoration of digital images with known space-variant blurs from conventional optical systems.Visual Information Processing VIII. 1999. Orlando, FL, USA: SPIE.p.71-79
[126] Costello, T.P.,W.B. Mikhael, Efficient restoration of space-variant blurs from physical optics by sectioning with modified Wiener filtering. Digital Signal Processing, 2003. 13: p.1-22.
[127] Trussell, H.,M. Civanlar, The feasible solution in signal restoration. Acoustics, Speech, and Signal Processing [see also IEEE Transactions on Signal Processing], IEEE Transactions on, 1984. 32(2): p.201-212.
[128] 冈萨雷斯, 数字图像处理(MATLAB 版). 2005.473.
[129] 孔祥龙, 李玉同, 远晓辉等, Lucy-Richardson 算法用于针孔图像的恢复. 2006. 55(5): p.2364-2370.
[130] 王鸿南, 钟文, 汪静等, 图像清晰度评价方法研究. 2004. 9(7): p.828-831.
[131] 朱孔凤, 姜威, 王端芳等, 一种新的图像清晰度评价函数. 2005. 34(4): p.464-468.
[132] 曹茂永, 孙农亮 郁道银, 离焦模糊图像清晰度评价函数的研究. 2001. 22(3): p.259-268.
[133] 袁珂 徐蔚鸿, 基于图像清晰度评价的摄像头辅助调焦系统. 2006. 33(1): p.141-144.
[134] Xin, L. Blind image quality assessment.Image Processing. 2002. Proceedings. 2002 International Conference on. 2002.p.I-449-I-452 vol.1
[135] Eskicioglu, A.M.,P.S. Fisher, Image quality measures and their performance. Communications, IEEE Transactions on, 1995. 43(12): p.2959-2965.
[136] Damera-Venkata, N., T.D. Kite, W.S. Geisler, et al., Image quality assessment based on a degradation model. Image Processing, IEEE Transactions on, 2000. 9(4): p.636-650.
[137] 汪孔桥, 数字图像的质量评价. 2000. 19(5): p.14-16.
[138] 刘晶晶, 晏磊, 赵红颖等, 数字图像运动模糊复原质量评价方法研究. 2007(1): p.13-15.
[139] 杨守义 罗伟雄, 一种基于高阶统计量的图像质量客观评价方法. 2001. 21(5): p.610-613.
[140] 丁绪星, 朱日宏 李建欣, 一种基于人眼视觉特性的图像质量评价. 2004. 9(2): p.190-194.
[141] 魏政刚, 袁杰辉 蔡元龙, 一种基于视觉感知的图像质量评价方法. 1999.27(4): p.79-82.
[142] 周 建 鹏 杨 义 先 , 一 种 图象 质量 的 感 知 测 量 方法 . 1998. 3(3): p.200-204.
[143] Hafner, J., H.S. Sawhney, W. Equitz, et al., Efficient color histogram indexing for quadratic form distance functions. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1995. 17(7): p.729-736.
[144] Suryani, L.,L. Guojun. Spatial statistics for content based image retrieval. 2003.p.155-159
[145] 飞思科技产品研发中心, MATLAB6.5 辅助小波分析与应用. 2003, 北京: 电子工业出版社.283.
[146] 周贤, 姜威 朱孔凤, 基于小波多分辨率分析的自动聚焦算法. 2006. 23(2): p.104-134.