摘要
图像增强是指以满足特定应用需求为目的,突出图像中感兴趣区域信息,抑制或去除其他信息,针对不同的应用而异的图像分析识别预处理,其目标是变换原图像信息为更加适合人机辨识的系列方法。对图像质量的要求也随着多媒体技术和产品的不断发展和在各领域的广泛应用而不断提高。而通常图像在获取过程中受成像设备、场景动态范围、光照条件等因素影响,使得图像质量下降,甚至于影响后续的人机图像分析识别。为此,需要对图像进行动态范围调整、对比度增强、彩色图像增强处理及视觉感知一致性等方面的处理来获得高质量的图像。论文以具体应用目标要求为基准,通过对图像的视觉效果增强相关理论和技术方法的深入研究,分析其在实际应用中存在的问题和缺点,进一步提出相应的改进增强算法。主要包括对彩色图像本身的增强、基于图像域的多曝光图像融合增强以及基于频率域的多传感器图像融合增强。
本文的主要创新点:
1.提出一种基于视觉感知的色调映射(Tone Mapping, TM)彩色图像增强方法。针对人类视觉的全局和局部感知自适应性,通过改进相应的S型色调映射函数,有效实现图像对比度增强。为克服直接在RGB彩色空间中做增强处理后色彩失真和不自然的感观效果,通过将原图像变换到La*b*颜色空间中再进行增强处理,实验结果表明该方法能保持彩色一致性和实现图像快速增强。
2.提出一种结合Retinex理论和遗传算法(Genetic Algorithm, GA)优化的快速彩色图像增强方法。将基于灰度图像的传统图像增强方法直接推广应用到彩色图像增强,必然会破坏图像的彩色平衡,造成色彩感知的不协调性。为此,提出一种基于遗传算法优化的快速对比度增强算法,结合Retinex理论对图像的亮度进行处理,消除光晕,最后重建增强后的彩色图像克服了色彩不协调问题。实验结果表明使用所提出的方法增强后的彩色图像视觉质量得以提高且优于其他传统的技术方法,并展示了其有效性和鲁棒性。
3.提出一种基于梯度信息的多曝光融合(Multi-exposure Fusion)高动态范围图像(High Dynamic Range Image, HDRI)合成方法。为克服通用多曝光融合增强中权值平均等方法中出现的不考虑信息重要性及邻域像素关系所造成的细节损失和模糊等问题,提出依赖于图像曝光质量评估及梯度域信息进行权值设计,对图像进行融合,实验结果表明增强的图像兼具原始图像在暗区和亮区的相应细节,图像整体效果符合人类视觉感知特性要求。
4.提出一种基于分块去混淆(Ghost Removal)的多曝光融合算法。在处理动态多曝光图像融合增强过程中,最大的问题是如何解决由于运动所产生的混淆现象。本文通过提出基于梯度上升优化处理的自适应分块方法,并结合形态学原理,调整分块大小及动态区域块的权值,最终达到混淆去除的目的。同时利用Gaussian中心函数窗口滤波,去除在分块融合过程中引入的块边缘不连续性痕迹。实验结果表明该方法能有效增强多曝光图像并去除混淆问题。
5.提出基于双变换的可见光视觉图像与红外图像增强方法。针对低光照或是伪装遮挡图像在可见光视觉图像与红外图像序列成像的相关特性及融合处理中存在的问题,提出一种基于亮度预增强处理的二代Curvelet与Harr小波双变换权值系数融合处理机制来有效保留边缘信息和图像细节信息的增强方法。实验结果表明所提出的方法提高了融合图像的视觉感知质量,增强后的图像为遮挡和伪装目标的检测和定位提供了更为有效空间环境。
Image enhancement processing is related with a series of techniques to improveinformation of the focused region, weaken or remove the unnecessary information, oreven to convert the whole image into more suitable information model for human andmachine post-processing such as perception, analysis and recognition in the specialapplication fields. The design of the image enhancement algorithms is focused on thespecial goal of the application. The more requirements for higher quality images in suchimage applications have increased rapidly with the deeper development and widerapplications of the multimedia technologies and products. However, the visual effect ofthe image is affected by many internal and external conditions with the digital imagecapture device, dynamic range in the scenes and light condition. Sometimes thedegraded image is even hard to recognize for human and machine analysis. In order toobtain high quality images do with the processing of the image dynamic rangeadjustment, contrast stretch, color image enhancement and visual perceptionconsistency.
Several theoretical methods and techniques of image enhancement are deeplystudied in the dissertation which based on the requirements of the special applications.And further improved algorithms are proposed in the dissertation by analysis of theirproblems and disadvantages in image processing applications. The main work of thedissertation includes enhancement of the color image itself, multi-exposure imagesfusion enhancement based on image domain, and multi-sense images fusionenhancement based on frequency domain.
The main innovation of the dissertation:
1. A true color image enhancement method is proposed based on the improvementof the visual perception with the tone mapping operation. The proposed methodimprove the contrast of the image adaptively by modified the S-shaped function to suitfor human eye percepts the image in global or local scene information. We firstlyconvert the original image from RGB color space into La*b*color space and do theenhancement in the luminance channel, and in the end combined with the chromaticinformation to overcome the color distortion and unnatural color perception effectsintroduced by the image processing directly in the RGB three channels. Colorconsistency maintaining and achieving adaptive fast image enhancement effects are provided in the resulting images by the proposed algorithm.
2. A fast image enhancement method which combined Retinex theory and geneticalgorithm optimization is proposed for color image enhancement. The proposed methodcan disregard the shortcomings introduced by application the traditional imageenhancement methods on gray-scale image expand directly to color image enhancement.So use the Retinex method to decompose the brightness and refection information andapply the genetic algorithm optimization corresponding parameters to enhance theimage brightness information. Finally the proposed method compared with the othermethods in the enhanced result color images is more effective and robust for colorimage enhancement.
3. A high dynamic range (HDR) imaging method is proposed by usingmulti-exposure images fusion on their gradient domain information. The proposedmethod takes advantage of the gradient indicates the significant information andneighbor pixels relationship among the multi images in order to design a better weightmatrix for improving visual quality of the fusion image, and overcome the loss ofdetails and fuzzy effects from the conventional weight-averaged methods, finallyacquires an enhanced image with details both improved in brighter and darker regions.
4. A free of ghost multi-exposure fusion method is proposed based on image block.The key issue in the processing of multi-exposure fusion image enhancement indynamic scenes is how to remove the ghost due to the movement. The method based ongradient-ascent optimization perform appropriate block, and combine morphologicalopen/close operations, gradient magnitude and gradient direction relative differencebuild objective function for removing ghost. Then fusion image is filtered byGaussian-center blend function to smooth the block boundary artifacts.
5. An image enhanced method based on dual-transform is proposed for visualimage and infrared (IR) image fusion. The method based on the secondgenerated-Curvelet and Harr wavelet transform acquired the fusion weight coefficient toeffectively preserve edge and details information in the images for visual and IR imageswith poor-light condition and targets camouflaged in the scenes. The enhanced resultsboth provide more clear space environment for target localization and better visualperception for target recognition.
引文
[1] R. C. Gonzalez, R. E. Woods. Digital Image Processing[M]. Person Prentice Hall, New Jersey,2008
[2] Y. B. Rao, L. T. Chen. A survey of video enhancement techniques[J]. International Journal onElectrical Engineering and Informatics,2012,3(1):71-99
[3]饶云波.夜间视频增强的关键技术研究[D].成都:电子科技大学,2012
[4] E. Reinhard, G. Ward, S. Pattanaik, et al. High Dynamic Range Imaging: Acquisition, Displayand Image-based Lighting[M](2nd Edition). Morgan Kau_man,2010
[5] P. Oakley, J. H. Bu. Correction of simple contrast loss in color images[J]. IEEE Transactions onImage Processing,2007,16(2):511-522
[6] K. Narasimhan, C. R. Sudarshan, R. Nagarajan. A comparison of contrast enhancementtechniques in poor illuminated gray level and color images[J]. International Journal ofComputer Applications,2011,25(2):17-25
[7] A. Tarik, D. Salih, A. Yucel. A histogram modification framework and its application for imagecontrast enhancement[J]. IEEE Transactions on Image Processing,2009,18(9):1921-1935
[8] Q. Wang, R. K. Ward. Fast image/video contrast enhancement based on weighted thresholdedhistogram equalization[J]. IEEE Transactions on Consumer Electronics,2007,53(2):757-764
[9] Z.-Y. Chen, B. R Abidi, D.L Page, et al. Gray-level grouping (GLG): an automatic method foroptimized image contrast enhancement–part I: the basic method[J]. IEEE Transactions onImage Processing,2006,15(8):2290-2302
[10] T. Gabriel, F. T. Daniel, P. Stephen. Histogram specification: s fast and flexible method toprocess digital images[J]. IEEE Transactions on Instrumentation and Measurement,2011,60(5):1565-1578
[11] Y. M. Li, Y. B. Rao, L. T. Chen, et al. Color image enhancement using tone mapping[J]. Journalof Digital Content Technology and its Applications,2012,6(22):631-639
[12] J. A. Stark. Adaptive image contrast enhancement using Generalizations of histogramequalization[J]. IEEE Transactions on Image Processing,2000,9(5):269-280
[13] D. Menotti, L. Najman, J. Facon, et al. Multi-histogram equalization methods for contrastenhancement and brightness preserving[J]. IEEE Transactions on Consumer Electronics,2007,53(3):1186-1194
[14] N. C. Gallagher, G. L. Wise. A theoritical analysis of the properties of median filters[J]. IEEETransactions on Acoustics, Speech and Signal Processing,1981,29(6):1136-1141
[15] T. Loup. An adaptive weighted median filter for speckle suppression in medical ultrasonicimage[J]. IEEE Transactions on Circuits System,1989,36(1):129-135
[16] K. Mustafa, K. M. Alper, B. Gozde. An adaptive speckle suppression filter for medicalultrasonic imaging[J]. IEEE Transactions on Medical Imaging,1995,14(2):283-292
[17] J. H. Wang, W. J. Liu. Histogram-based fuzzy filter for image restoration[J]. IEEE Transactionson System, Man, Cybern, Part B,2002,232(2):230-238
[18] M. Juneja, R. Mohana. An improved adaptive median filtering method for impulse noisedetection[J]. International Journal of Recent Trends in Engineering,2009,1:274-278
[19] I. Pollak, A. S. Willsky, H. Krim. Image segmentation and edge enhancement with stabilizedinverse diffusion equations[J]. IEEE Transactions on Image Processing,2000,9(2):256-267
[20] H. L. Eng, K. K. Ma. Noise adaptive soft-switching median filter[J]. IEEE Transactions onImage Processing,2001,10(2):242-251
[21] J. Duan, M. Bressan, C. Dance, et al. Tone-mapping high dynamic range images by novelhistogram adjustment[J]. Pattern Recognition,2010,43(5):1847-1862
[22] P. Ganzalop, M. Jesus. Wavelet-based image fusion tutorial[J]. Pattern Recognition.2004,8(37):1855-1872
[23] S. Li, B. Yang. Multifocus image fusion by combining curvelet and wavelet transform[J].Pattern Recognition Letters,2008,29:295-301
[24] A. Borsdorf, R. Raupach, T. Flohr, et al. Wavelet based noise reduction in CT-images usingcorrelation analysis[J]. IEEE Transactions on Medical Imaging,2008,27(12):1685-1703
[25] K. Tsai, J. W. Ma, D. T. Ye, et al. Curvelet processing of MRI for local image enhancement[J].International Journal for Numerical Methods in Biomedical Engineering,2012,28(6):661-670
[26] M. H. Asmare, V. S. Asirvadam, L. Iznita. Image enhancement by fusion in contourlettransform[J]. International Journal on Electrical Engineering and Informatics,2010,2(1):29-42
[27] T. Stathaki. Image Fusion Algorithms and Applications[M]. Academic Press is an imprint ofElsevier,2008
[28] A. Toet. Natural color mapping for multi band night-vision imagery[J]. Information Fusion,2003,4:155-166
[29] P. Shah, B. C. S. Reddy, S. N. Merchant, et al. Context enhancement to reveal a camouflagedtarget and to assist target localization by fusion multispectral surveillance videos[J]. Signal,Image and Video Processing,2011,8:1-16
[30] C. Pohl, J. L. V. Genderen. Multisensor image fusion in remote sensing: concepts, methods andapplications[J]. International Journal of Remote Sensing,1998,19(5):823-854
[31] M. Li, W. Cai, Z. Tan. A region-based multi-sensor image fusion scheme using pulse-coupledneural network[J]. Pattern Recognition Letters,2006,27(16):1948-1956
[32] T. Mertens, J. Kautz, T. V. Reeth. Exposure fusion: a simple and practical alternative to highdynamic range photography[J]. Computer Graphics forum,2009,28:161-171
[33] A. A. Goshtasby. Fusion of multi-exposure images[J]. Image and Vision Computing,2005,23:611-618
[34] S. T. Li, X. D. Kang. Fast multi-exposure image fusion with media filter and recursive filter[J].IEEE Transactions on Consumer Electronic,2012,58(2):626-632
[35] K. Jacobs, C. Loscos, G. Ward. Automatic high dynamic range image generation for dynamicscenes[J]. IEEE Computer Graphics and Applications,2008,28:84-93
[36] E. A. Khan, A. O. Akyuz, E. Reinhard. Robust generation of high dynamic range images[C].Proceedings of International Conference on Image Processing, Georgia, USA,2006,2005-2008
[37] Y. M. Li, L. T. Chen, Y. B. Rao, et al. An efficient color image fusion algorithm[C].7th IEEEInternational Conference on System of Systems Engineering (SoSE2012), Genoa,Italy,2012,153-156
[38] K. Kotwal, S. Chaudhuri. An optimization-based approach to fusion of multi-exposure, lowdynamic range images[C]. Proceedings of the14th International Conference on InformationFusion, Chicago, USA,2011,1-7
[39] W. Zhang, W. K. Cham. Gradient-directed multiexposure composition[J]. IEEE Transactionson Image Processing,2012,21(4):2318–2323
[40] N. Jacobson, M. Gupta, J. Cole. Linear fusion of image sets for display[J]. IEEE Transactionson Geoscience. Remote Sense,2007,45:3277-3288
[41] Z. Wang, A. C. Bovik, H. R. Sheikh, et al. Image quality assessment: from error visibility tostructural similarity[J]. IEEE Transactions on Image Processing,2004,13:600–612
[42] X. L. Chen, X. K. Yang, S. B. Zheng, et al. New image quality assessment method usingwagelet leader pyramids[J]. Optical Engineering,2011,50(6),067011:1-8
[43] C. Ramesh, T. Ranjith. Fusion performance measures and a lifting wavelet transform basedalgorithm for image fusion[C]. in Processing of the5th International Conference onInformation Fusion, Sunnyvale, USA,2002,1:317-320
[44] G. H. Qu, D. Zhang, P. Yan. Information measure for performance of image fusion[J].Electronics Letters,2002,38(7):313–315
[45] C. S. Xydeas, V. Petrovic. Objective image fusion performance measure[J]. Electronics Letters,2000,36(4):308-309
[46] Q. Wang, Y. Shen, Y. Zhang, et al. Fast quantitative correlation analysis and informationdeviation analysis for evaluating the performances of image fusion techniques[J]. IEEETransactions on Instrumentation and Measurement,2004,53(5):1441-1447
[47] T. Bank. Evolutionary Algorithms in Theory and Practice[M]. Oxford University Press, NewYork,1996
[48] Y. M. Li, Y. B. Rao, L. T. Chen, et al. Color image enhancement method using geneticalgorithm[J]. Journal of Computational Information Systems,2012,8(19):8065-8073
[49] D. N. Chun, H. S. Yang. Robust image segmentation using genetic algorithm with a fuzzymesure[J]. Pattern Recognition,1996,29(7):1195-1996
[50]周激流,吕航.一种基于新型遗传算法的图像自适应增强算法的研究[J].计算机学报,2001,24(9):1-6
[51] F. H. F. Leung, H. K. Lam, S. H. Ling, et al. Tuning of the Structure and Parameters of a NeuralNetwork Using an Improved Genetic Algorithm[J]. IEEE Transactions on Neural Networks,2003,14(1):79-88
[52]姜允志,郝志峰,林智勇,等.基于分块采样和遗传算法的自动多阈值图像分割[J].计算机辅助设计与图形学学报,2011,23(11):1860-1868
[53] J. Tumblin, H. E. Rushmeier. Tone reproduction for realistic images[J]. IEEE Transactions onComputer Graphics and Applications,1993,13(6):42-48
[54] A. Scheel, M. Stamminger, H. P. Seidel. Tone reproduction for interactive walkthroughs[J].Computer Graphics Forum,2000,19(3):301-312
[55] J. H. Kim, H. Kim, S. J. Ko. New visualization method for high dynamic range images in lowdynamic range devices[J]. Optical Engineering,2011,50(10):107005
[56] G. W. Larson, H. Rushmeier, C. Piatko. A visibility matching tone reproduction operator forhigh dynamic range scenes[J]. IEEE Transactions on Visualization and Computer Graphics,1997,3(4):291-306
[57] E. Reinhard, M. Stark, J. Ferweda. Photographic tone reproduction for high digital images[J].ACM Transactions on Graphics,2002,21(3):267-276
[58] R. Fattal, D. Lischinski, M. Werman. Gradient domain high dynamic range compression[C].SIGGRAPH2002Conference on Graphics Proceedings, San Antonio, USA,2002,249-256
[59] F. Durand, J. Dorsey. Fast bilateral filtering for the display of high dynamic range image[C].SIGGRAPH2002Conference on Graphics Proceedings, San Antonio, USA,2002,257-265
[60] T. L. Liu, C. S. Fuh. Tone reproduction: a perspective from luminance-driven perceptualgrouping[J]. International Journal of Computer Vision,2005,65(1):73-96
[61] E. Reinhard, K. Devlin. Dynamic range reduction inspired by photoreceptor physiology[J].IEEE Transactions on Visualization and Computer Graphics,2005,11(1):13-24
[62] J. Duan, G. Qui. Fast tone mapping for high dynamic range images[C]. in Processing of17thInternational Conference on Pattern Recognition, Bristol, UK,2004,2:847-850
[63] Y. Li, L. Sharan, E. H. Adelson. Compressing and companding high dynamic range images withsubband architectures[J]. ACM Transactions on Graphics,2005,24(3):836-844
[64] Y. Monobe, H. Yamashita, T. Kurosawa, et al. Dynamic range compression preserving localimage contrast for digital video camera[J]. IEEE Transactions on Consumer Electronics,2005,51(1):1-10
[65] J. W. Lee, R. H. Park. Tone mapping using color correction function and image decompositionin high dynamic range imaging[J]. IEEE Transactions on Consumer Electronics,2010,56(4):2772-2780
[66] H. Shin, T. Yu, Y. Ismail, et al. Rendering high dynamic range images by using integratedglobal and local processing[J]. Optical Engineering,2011,50(11):117002
[67] A. K. Vishwakarma, A. Mishra. Color image enhancement techniques: a critical review[J].Indian Journal of Computer Science and Engineering,2012,3(1):39-45
[68] M. Kaur, J. Kaur, J. Kaur. Survey of contrast enhancement techniques based on histogramequalization[J]. International Journal of Advanced Computer Science and Applications,2011,2(7):137-141
[69] N. Sengee, A. Sengee, H.-K. Choi. Image contrast enhancement using bi-histogramequalization with neighborhood metrics[J]. IEEE Transactions on Consumer Electronics,2010,56(4):2727-2734
[70] Y. T. Kim. Contrast enhancement using brightness preserving bi-histogram equalization[J].IEEE Transactions on Consumer Electronics,1997,43(1):1-8
[71] Y. Wang, Q. Chen, B. Zhang. Image enhancement based on equal area dualistic sub-imagehistogram equalization method[J]. IEEE Transactions on Consumer Electronics,1999,45(1):68-75
[72] S. D. Chen, A. Ramli. Minimum mean brightness error bi-histogram equalization in contrastenhancement[J]. IEEE Transactions on Consumer Electronics,2003,49(4):1310-1319
[73] S.-D. Chen, A. Ramli. Contrast enhancement using recursive Mean-Separate histogramequalization for scalable brightness preservation[J]. IEEE Transactions on ConsumerElectronics,2003,49(4):1301-1309
[74] M. Abdullah-Al-Wadud, M. H. Kabir, M. A. A. Dewan, et al. A dynamic histogram equalizationfor image contrast enhancement[J]. IEEE Transactions on Consumer Electronics,2007,53(3):593-600
[75] H. Ibrahim, N. S. P. Kong. Brightness preserving dynamic histogram equalization for imagecontrast enhancement[J]. IEEE Transactions on Consumer Electronics,2007:1752-1758
[76] N. S. P. Kong, H. Ibrahim. Color image enhancement using brightness preserving dynamichistogram equalization[J]. IEEE Transactions on Consumer Electronics,2008,54(4):1962-1968
[77] G. J. Braun, M. D. Fairchild. Image lightness rescaling using sigmoidal contrast enhancementfunctions[J]. Color Imaging: Device-Independent Color, Color Hardcopy, and Graphic Arts IV,San Jose, CA,1998,3648(1):96-107
[78] P. Ledda, L. P. Santos, A. Chalmers. A local model of eye adaptation for high dynamic rangeimagines[C]. in Proceedings of AFRIGRAPH, Cape Town, South Africa,2004,151-160
[79] L. Meylan, S. Süsstrunk. High dynamic range image rendering with a Retinex-based adaptivefilter[J]. IEEE Transaetions on Image Processing,2006,15(9):2820-2830
[80] L. Meylan, D. Alleysson, S. Süsstrunk. Model of Retinal local adaptation for the tone mappingof color filter array images[J]. Journal of the Optical Society of AmericaA,2007:24(9):2807–2816
[81] S. Lee. An efficient content-based image enhancement in the compressed domain using Retinextheory[J]. IEEE Transactions on Circuits and Systems for Video Technology,2007,17(2):199-213
[82] D. Jobson, Z. Rahman, G. Woodell. A multiscale retinex for bridging the gap between colorimages and the human observation of scenes[J]. IEEE Transactions on Image Processing,1997,6(7):965-976
[83] E. H. Land, J. J. McCann. Lightness and Retinex theory[J]. Journal of the Optical Society ofAmerica,1971,61:1-11
[84] E. H. Land. The retinex theory of color vision[J]. Scientific American,1977,237(6):108-129
[85] M.-S. Shyua, J.-J. Leou. A genetic algorithm approach to color image enhancement[J]. PatternRecognition,1998,7(31):871-880
[86] M. Paulinas, A. Usinskas. A survey of genetic algorithms applications for image enhancementand segmentation[J]. Information Technology and Control,2007,36(3):278-284
[87] A. Mustafi, P. K. Mahanti. An optimal algorithm for contrast enhancement of dark images usinggenetic algorithms[J]. Computer and Information Science, Studies in computational intelligence,2009,208:1-8
[88] F. Saitoh. Image contrast enhancement using genetic algorithm[C]. In Proceedings of the IEEEInternational Conference on System, Man and Cybern, Tokyo, Japan,1999,4:899-904
[89] S. Hashemi, S. Kiani, N. Noroozi, et al. An image contrast enhancement method based ongenetic algorithm[J]. Pattern Recognition Letters,2010,31(13):1816-1824
[90] C. S. Lee, S.–M. Guo, C.-Hsu. Genetic-based fuzzy image filter and its applications to imageprocessing[J]. IEEE Transactions on Systems, Man, and Cybernetics,2005,35(4):694-711
[91] L. Wang, S. Tingzhi. An improved adaptive genetic algorithm and its application to imagesegmentation[J]. IEEE Transactions on Image Processing,1996,5(3):142-149
[92] C. Muntaenu, A. Rosa. Towards automatic image enhancement using genetic algorithms[C].Proceedings of the congress on evolutionary computation, California, USA,2000,2:1535-1542
[93] C. Muntaenu, A. Rosa. Color image enhancement using evolutionary principles and the retinextheory of color constancy[C]. Proceedings of the IEEE signal processing society workshop onneural networks for signall processing,2001,393-402
[94] Z. J. Chang, X. D. Wang. Global and local contrast enhancement for image by geneticalgorithm and wavelet neural network[J]. LNCS Book Series, Springer,2006,42(34):910-919
[95] R. Fries, J. Modestino. Image enhancement by stochastic homomorphic filtering[J]. IEEETransactions on Aeousties,Speech, and Signal Processing,1979,27(6):625-637
[96] K. Delac, M. Grgic, T. Kos. Sub-image Homomorphic Filtering technique for improving facialidentification under difficult illumination conditions[C]. International Conference on Systems,Signals and Image Processing, Budapest, Hungary,2006,95-98
[97] A. Polesel, G. Ramponi, V. J. Mathews. Image enhancement via adaptive unsharp masking[J].IEEE Transactions on Image Processing,2000,9(3):505-510
[98] S. Guillon,P. Baylou,M. Najim,et al. Adaptive nonlinear filters for2D and3D imageenhancement[J]. Signal Proeessing,1998,67(3):237-254
[99] S. Kim, W. Kang, E. Lee, et al. Wavelet-domain color image enhancement using filtereddirectional bases and frequency-adaptive shrinkage[J]. IEEE Transactions on ConsumerElectronics,2010,56(2):1063-1070
[100] T. J. Brown. An adaptive strategy for wavelet based image enhancement[C]. Proceedings ofIrish Machine Vision and Image Processing Conference, Helfast,2000:67-81
[101] A. Toet. Multi-scale color image enhancement[J]. Pattern Recobn, Letter,1992,13:167-174
[102] K. Q. Huang, Q. Wang, Z. Y. Wu. Natural color image enhancement and evaluation algorithmbased on human visual system[J]. Computer Vision and Image Understanding,2006,103:52-63
[103] J.-L. Starck, F. Murtagh, E. J. Candes, et al. Gray and color image contrast enhancement by thecurvelet transform[J]. IEEE Transactions on Image Processing,2003,12(6):706-717
[104] P. Debevec, J. Malik, Recovering high dynamic range radiance maps from photographs[C]. inProceedings of the SIGGRAPH97Conference, Los Angeles, USA,1997,369-378
[105] T. Mitsunaga, S. K. Nayar. High dynamic range imaging: spatially varying pixel exposures[C].in Proceedings of the IEEE International Conference on Computer Vision and PatternRecognition, Hilton Head Island,2000,472-479
[106] D. D. Wen. High dynamic range charge-coupled device[P]. USA. Patent,4873561,1989
[107] R. A. Street, High dynamic range segmented pixel sensor array[P]. USA. Patent,5789737,1998
[108] J. Tumblin, A. Agrawal, R. Raskar. Why I want a gradient camera[C]. in Proceedings of theIEEE International Conference on Computer Vision and Pattern Recognition, San Diego,2005,103-110
[109] M. W. Seo, S. H. Suh, T. Iida, et al. A low-noise high intrascene dynamic range CMOS imagesensor with a13to19b variable-resolution column-parallel folding-integration/cyclic ADC[J].IEEE Journal of Solid-State Circuits,2012,47(1):272-283
[110] M. W. Seo, T. Sawamoto, T. Akahori, et al. A low-noise high-dynamic-range17-b1.3-Megapixel30-fps CMOS image sensor with column-parallel two-stagefolding-integration/cyclic ADC[J]. IEEE Transactions on Electron Devices,2012,59(12):3396-3400
[111] G. Ward. Robust image registration for compositing high dynamic range photographs fromhand-held exposures[J]. Journal of Graphics Tools,2003,8:17-33
[112] C. Harris, M. Stephens. A combined corner and edge detector[C]. in Proceedings of the4thAlvey Vision Conference, Manchester,1988,147-152
[113] G. Lowe. Distinctive image features from scale-invariant keypoints[J]. International Journal ofComputer Vision,2004,60(2):91-110
[114] A. Srikantha, D. Sidibe. Ghost detection and removal for high dynamic range images: recentadvances[J]. Signal Processing: Image Communication,2012,27(6):650-662
[115] T. Grosch. Fast and robust high dynamic range image generation with camera and objectmovement[C]. in Proceedings of Vision, Modeling and Visualization Conference, Aachen,2006,277-284
[116] D. Sidibe, W. Puech, O. Strauss. Ghost detection and removal in high dynamic range images[C].2009European Signal Processing Conference (EUSIPCO-2009), Glasgow,2009,2240-2244
[117] T.-H. Min, R.-H. Park, S.-K. Chang. Histogram based ghost removal in high dynamic rangeimages[C]. in Proceedings of the International Conference on Multimedia and Expo-ICME,New York,2009,530-533
[118] F. Pece, J. Kautz. Bitmap movement detection: HDR for dynamic scenes[C]. in Proceedings ofVisual Media Production (CVMP), London,2010,1-8
[119] O. Gallo, N. Gelfand, W.-C. Chen, et al. Artifact-free high dynamic range imaging[C]. inProceedings of the IEEE International Conference on Computational Photography (ICCP2009),San Francisco, USA,2009,1-7
[120] M. A. Fischler, R. C. Bolles. Random sample consensus: a paradigm for model fitting withapplications to image analysis and automated cartography[J]. Communication of the ACM,1981,24:381-395
[121] Y.-S. Heo, K.-M. Lee, S.-U. Lee, et al. Ghost-free high dynamic range imaging[C]. inProceedings of the10th Asian conference on Computer vision (ACCV2010), Queenstown,2010,486-500
[122] Y. Boykov, O. Veksler, R. Zabih. Fast approximate energy minimization via graph cuts[J]. IEEETransactions on Pattern Analysis and Machine Intelligence,2001,23:1222-1239
[123] B. Lucas, T. Kanade. An iterative image registration technique with an application to stereovision[C]. in Proceedings of the7th International Joint Conference on Artificial Intelligence,Vancouver,1981,674-679
[124] S. Kang, M. Uyttendaele, S. Winder, et al. High dynamic range video[J]. ACM Transactions onGraphics,2003,22:319-325
[125] Y. B. Rao, W. Y. Lin, L. T. Chen. A global-motion-estimation-based method for nighttime videoenhancement[J]. Optical Engineering,2011,50(5):1-7
[126] A. Srikantha, D. Sidibe, F. Meriaudeau. An SVD-based approach for ghost detection andremoval in high dynamic range images[C].21st International Conference on PatternRecognition, Tsukuba, Japan,2012,380-383
[127] P. Burt, T. Adelson. The Laplacian pyramid as a compact image code[J]. IEEE Transactions onCommunication,1983,31:532-540
[128] M. Pedone, J. Heikkil. Constrain propagation for ghost removal in high dynamic rangeimages[C]. in Proceedings of the IEEE International Conference on Computer Vision Theoryand Applications, Funchal,2008,36-41
[129] E. P. Bennett, J. L. Mason, L. Mcmillan. Multispectral bilateral videos fusion[J]. IEEETransactions on Image Processing,2007,16(5):1185-1194
[130] A. M. Waxman, A. N. Gove, D. A. Fay, et al. Color night vision: opponent processing in thefusion of visible and IR imagery[J]. Neural Networks Letter,1997,10(1):1-6
[131] B. Stoklasa, J. Rehacek, Z. Hradil. Adaptive IR and VIS image fusion[C]. Proceedings ofMultisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications,2012, Maryland, USA,2012,84070Y:1-8
[132]王睿,王林,袁艳.基于区域灰度统计信号处理的图像融合方法[J].北京航空航天大学学报,2011,40(6):140-144
[133] P. Shah, S. N. Merchant, U. B. Desai. Fusion of surveillance images in infrared and visible bandusing curvelet, wavelet and wavelet packet transform[J]. International Journal WaveletMultiresolution and Information Processing,2010,8(2):271-292
[134]马东辉,薛群,柴奇,等.基于图像信息的红外与可见光图像融合方法研究[J].红外与激光工程,2011,40(6):1168-1171
[135] P. Shah, S. N. Merchant, U. B. Desai. Multifocus and multispectral image fusion based on pixelsignificatiance using multiresolution decomposition[J]. Signal Image Video Process,2013,7:95-109
[136] M. Ehlersa, S. Klonusa, P. J. Astrand, et al. Multi-sensor image fusion for pansharpening inremote sensing[J]. International Journal of Image and Data Fusion,2010,1(1):25-45
[137]付梦印,赵诚.基于二代Curvelet变换的红外与可见光图像融合[J].红外与毫米波学报,2009,28(4):254-258
[138]王金华.高动态范围场景可视化技术研究[D].北京:北京交通大学,2010
[139] G. L. Ward,H. Rushmeier,C. Piatko. A visibility matching tone reproduction operator for highdynamic range seenes[J]. IEEE Transactions on Visualization and Computer Graphics,1997,3(4):291-306
[140] P. Perez, M. Gangnet, A. Blake. Poisson image editing[J]. ACM Transaction on Graphics,2003,22:313-318
[141] R. Raskar, A. Ilie, J. Yu. Image fusion for context enhancement and video surrealism[C]. InProceedings of the International Symposium on Non-Photorealistic Animation and Rendering,Annecy,2004,85-94
[142] J. Takao, O. Masahiro, A. Nicol. Acquisition and encoding of high dynamic range images usinginverse tone mapping[C]. In Proceedings of the International Conference on Image Processing,San Antonio,2007,4:181-184
[143] E. P. Bennett, L. McMillan. Video enhancement using per-pixel virtual exposures[J]. ACMTransactions on Graphics,2005,24(3):845-852
[144] M. Hanmandlu, D. Jha. An optimal fuzzy system for color image enhancement[J]. IEEETransactions on Image Processing,2006,15(10):2956-2966
[145] M. Hanmandlu, O. P. Verma, N. K. Kumar, et al. A novel optimal fuzzy system for color imageenhancement using bacterial foraging[J]. IEEE Transactions on Instrumentation andMeasurement.2009,58(8):2867-2879
[146] N. Reyadh, A.-S. Alaa. Color image enhancement using steady state genetic algorithm[J].World of Computer Science and Infformation Technology Journal.2012,2(6):184-192
[147] S. C. Pei, Y. M. Chiu. Background adjustment and saturation enhancement in ancientChinese paintings[J]. IEEE Transactions on Image Processing,2006,15(10):3230-2234
[148] Y. B. Rao, L. T. Chen. Illumination-based nighttime video contrast enhancement using geneticalgorithm[J]. Multimedia Tools and Applications,2012,9:1-20
[149] R. Kimmel, M. Elad, I. Sobel. A variational framework for Retinex[J]. International Journal ofComputer Vision,2003,52(1):7-23
[150] R. Kimmel, M. Elad, D. Shaked, et al. Sapce-dependent color gamut mapping: a variationalapproach[J]. IEEE Transactions on Image Processing,2005,14(6):796-803
[151] J. D. Tubbs. A note on parametric image enhancement[J]. Pattern Recognition,1997,30(6):617-621
[152] C. M. Tsai, Z. M. Yeh. Contrast enhancement by automatic and parameter-free piecewise lineartransformation for color images[J]. IEEE Transactions on image processing,2009,24(11):213-219
[153] T. Li, H. Ngo, M. Zhang, et al. A multi-sensor image fusion and enhancement system forassisting drivers in poor lighting conditions[C]. In Proceedings of the34th Applied Imagery andPattern Recognition workshop, Washington, USA,2005,106-113
[154] S. Fleishman, et al. Video operations in the gradient domain[R]. Tel-Aviv University, Tel-Aviv,Israel,2004
[155] A. Agrawal, R. Raskar, S. K. Nayar, et al. Removing photography artifacts using gradientprojection and flash-exposure sampling[J]. ACM Transactions on Graphics,2005,24:828-835
[156] M. Kazhdan, H. Hoppe. Streaming multigrid for gradient-domain operations on large images[J].ACM Transactions on Graphics,2008,27(3):1-10
[157] W.-H. Cho, K.-S. Hong. Extending dynamic range of two color images under differentexposures[C]. Proceedings of the17thInternational Conference on Pattern Recognition,Cambridge,2004,4:853-856
[158] A. Eden, M. Uyttendaele, and R. Szeliski. Seamless image stitching of scenes with largemotions and exposure differences[C]. IEEE Computer Society Conference on Computer Visionand Pattern Recognition, New York, USA,2006,2:2498-2505
[159]孙明超,张崇,刘晶红.多尺度图像增强可见光与红外图像融合[J].吉林大学学报(工学版),2012,42(3):738-742
[160]李晖晖,郭雷,刘航.基于二代curvelet变换的图像融合研究[J].光学学报,2006,26(5):657-662
[161] E. J. Candès, D. L. Donoho. Newtight frames of curvelets and optimal representations ofobjects with C2singularities[J]. Communication on Pure and Application Math,2004,57(2):219-266
[162] E. J. Candès, L. Demanet, D. L. Donoho, et al. Fast diserete curvelet transforms[R]. Appliedand Computational Mathematic California institute of Technology,2005,1-43