改进BPDN的图像去雨雪应用研究
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摘要
在视频图像数据采集时,往往受到天气等因素的影响,为进一步研究视频图像数据的主要信息增加了困难。雨、雪天气作为视频图像数据预处理中最为困难的研究,近几年一直是各学者研究的课题。针对传统基追踪去噪(BPDN)算法没有考虑雨、雪图像的局部特征问题,提出一种基于改进BPDN的图像去雨雪算法。将局部特征约束理论引入图像去雨雪中,主要是在精炼雨图提取的稀疏系数优化问题求解中加入训练图像数据的局部信息,以达到提高雨线的识别,进而提高图像的雨线去除效果。引入局部信息的优化问题可以推导为二次规划问题,为使用BPDN算法提供了理论支持。合成和真实图像去雨实验结果表明,改进的BPDN算法在算法收敛性、精炼雨图识别、图像去雨效果上优于传统BPDN算法所得到的结果。
When video image data is collected, it is often affected by weather and other factors, which makes it difficult to further study the information in the video image data. In recent years, more and more researchers have paid more attention to video image data preprocessing about rain and snow weather which is one of the most difficult research topics.In order to solve the problem that the traditional Basis Pursuit Denoising(BPDN)algorithm has not considered the local features of rain and snow images, an image deraining and desnowing algorithm based on improved BPDN is proposed. In order to improve the rain streaks recognition rate and thus the rain streaks removal effect of an image, the local feature constraint theory is introduced into the image deraining and desnowing by adding the local information of the training image data to the refined rain map when solving the sparse coefficient optimization problem. The obtained optimization problem by introducing the local information of the training image data can be viewed as a quadratic programming problem, which provides a theoretical base for using BPDN algorithm. The deraining and desnowing experimental results on synthetic and real images show that the improved BPDN algorithm is better than the traditional BPDN algorithm in algorithm convergence, refined rain map recognition and rain streaks removal effect of an image.
When video image data is collected, it is often affected by weather and other factors, which makes it difficult to further study the information in the video image data. In recent years, more and more researchers have paid more attention to video image data preprocessing about rain and snow weather which is one of the most difficult research topics.In order to solve the problem that the traditional Basis Pursuit Denoising(BPDN)algorithm has not considered the local features of rain and snow images, an image deraining and desnowing algorithm based on improved BPDN is proposed. In order to improve the rain streaks recognition rate and thus the rain streaks removal effect of an image, the local feature constraint theory is introduced into the image deraining and desnowing by adding the local information of the training image data to the refined rain map when solving the sparse coefficient optimization problem. The obtained optimization problem by introducing the local information of the training image data can be viewed as a quadratic programming problem, which provides a theoretical base for using BPDN algorithm. The deraining and desnowing experimental results on synthetic and real images show that the improved BPDN algorithm is better than the traditional BPDN algorithm in algorithm convergence, refined rain map recognition and rain streaks removal effect of an image.
引文
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[5] Sun S H,Fan S P,Wang Y C F.Exploiting image structural similarity for single image rain removal[C]//IEEE International Conference on Image Processing,2015:4482-4486.
[6] Chen Y L,Hsu C T.A Generalized low-rank appearance model for spatio-temporally correlated rain streaks[C]//IEEE International Conference on Computer Vision,2013:1968-1975.
[7] Luo Y,Xu Y,Ji H.Removing rain from a single image via discriminative sparse coding[C]//IEEE International Conference on Computer Vision,2015:3397-3405.
[8] Li Y,Tan R T,Guo X,et al.Rain streak removal using layer priors[C]//IEEE Conference on Computer Vision and Pattern Recognition,2016:2736-2744.
[9] Fu X,Huang J,Zeng D,et al.Removing rain from single images via a deep detail network[C]//IEEE Conference on Computer Vision and Pattern Recognition,2017:1715-1723.
[10] Yang Wenhan,Tan R T,Feng Jiashi,et al.Deep joint rain detection and removal from a single image[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition,2017:1685-1694.
[11] Abdel-Hakim A E.A novel approach for rain removal from videos using low-rank recovery[C]//International Conference on Intelligent Systems,Modelling and Simulation,2015:351-356.
[12]朱林,郝元宏,蒋秀蓉.基于低秩稀疏分解的自适应运动目标检测算法[J].计算机工程与应用,2018,54(23):189-194.
[13] Kim J H,Sim J Y,Kim C S.Video deraining and desnowing using temporal correlation and low-rank matrix completion[J].IEEE Transactions on Image Processing,2015,24(9):2658-2670.
[14] Yu K,Zhang T.High dimensional nonlinear learning using local coordinate coding[J].arXiv:0906.5190,2009.
[15] Wang J,Yang J,Yu K,et al.Locality-constrained linear coding for image classification[C]//IEEE Conference on Computer Vision&Pattern Recognition,2010.
[16] Li Z,Lai Z,Xu Y,et al.A locality-constrained and label embedding dictionary learning algorithm for image classification[J].IEEE Transactions on Neural Networks and Learning Systems,2015,28(2):278-293.
[17] Chen S S,Donoho D L,Saunders M A.Atomic decomposition by basis pursuit[J].SIAM Review,2001,43(1):129-159.
[18] Figueiredo M A T,Nowak R D,Wright S J.Gradient projection for sparse reconstruction:application to compressed sensing and other inverse problems[J].IEEE Journal of Selected Topics in Signal Processing,2008,1(4):586-597.
[19] After Effects CS6,Adobe Systems Inc.,San Jose,CA,USA[Z].2012.
[20] Sheikh H R,Bovik A C.Image information and visual quality[M].[S.l.]:IEEE Press,2006.
[21] Sheikh H R,Sabir M F,Bovik A C.A statistical evaluation of recent full reference image quality assessment algorithms[J].IEEE Transactions on Image Processing,2006,15(11):3440-3451.
[22] Wang Z,Bovik A C,Sheikh H R,et al.Image quality assessment:from error visibility to structural similarity[J].IEEE Transactions on Image Processing,2004,13(4).
[23] Wang Z,Bovik A C.A universal image quality index[J].IEEE Signal Processing Letters,2002,9(3):81-84.