面向单幅图像去雨的非相干字典学习及其稀疏表示研究

doi:10.11959/j.issn.1000-436x.2017149

Abstract

Abstract:

The incoherent dictionary learning and sparse representation algorithm was present and it was applied to single-image rain removal.The incoherence of the dictionary was introduced to design a new objective function in the dictionary learning,which addressed the problem of reducing the similarity between rain atoms and non-rain atoms.The divisibility of rain dictionary and non-rain dictionary could be ensured.Furthermore,the learned dictionary had similar properties to the tight frame and approximates the equiangular tight frame.The high frequency in the rain image could be decomposed into a rain component and a non-rain component by performing sparse coding based learned incoherent dictionary,then the non-rain component in the high frequency and the low frequency were fused to remove rain.Experimental results demonstrate that the learned incoherent dictionary has better performance of sparse representation.The recovered rain-free image has less residual rain,and preserves effectively the edges and details.So the visual effect of recovered image is more sharpness and natural.

Key words: incoherent dictionary, dictionary learning, sparse representation, single-image rain removal

CLC Number:

TP391.4

Hong-zhong TANG,Xiang WANG,Xiao-gang ZHANG,Xiao LI,Li-zhen MAO. Incoherent dictionary learning and sparse representation for single-image rain removal[J]. Journal on Communications, 2017, 38(7): 28-35.

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[1]	WANG Y , CHEN C , ZHU S ,et al. A framework of single-image derainingmethod based on analysis of rain characteristics[C]// IEEE International Conference on Image Processing. 2016: 4087-4091.
[2]	MI Z , SHANG J , ZHOU H ,et al. Image fusion-based video deraining using sparse representation[J]. Electronics Letters, 2016,52(18): 1528-1529.
[3]	YANG W , TAN R T , FENG J ,et al. Joint rain detection and removal via iterative region dependent multi-task learning[J]. arXiv preprint arXiv:1609.07769, 2016.
[4]	WU Q , ZHANG W , KUMAR B V K V . Raindrop detection and removal using salient visual features[C]// 19th IEEE International Conference on Image Processing. 2012: 941-944.
[5]	KIM J H , LEE C , SIM J Y ,et al. Single-image deraining using an adaptive nonlocal means filter[C]// 2013 IEEE International Conference on Image Processing. 2013: 914-917.
[6]	CHEN Y L , HSU C T . A generalized low-rank appearance model for spatio-temporally correlated rain streaks[C]// The IEEE International Conference on Computer Vision. 2013: 1968-1975.
[7]	LI Y , TAN R T , GUO X ,et al. Rain streak removal using layer priors[C]// The IEEE Conference on Computer Vision and Pattern Recognition. 2016: 2736-2744.
[8]	KANG L W , LIN C W , FU Y H . Automatic single-image-based rain streaks removal via image decomposition[J]. IEEE Transactions on Image Processing, 2012,21(4): 1742-1755.
[9]	HUANG D A , KANG L W , WANG Y C F ,et al. Self-learning based image decomposition with applications to single image denoising[J]. IEEE Transactions on Multimedia, 2014,16(1): 83-93.
[10]	LUO Y , XU Y , JI H . Removing rain from a single image via discriminative sparse coding[C]// The IEEE International Conference on Computer Vision. 2015: 3397-3405.
[11]	ELAD M , AHARON M . Image denoising via sparse and redundant representations over learned dictionaries[J]. IEEE Transactions on Image Processing, 2006,15(12): 3736-3745.
[12]	MAIRAL J , BACH F , PONCE J ,et al. Online dictionary learning for sparse coding[C]// The 26th Annual International Conference on Machine Learning. 2009: 689-696.
[13]	汤雅妃, 张云勇, 郭志斌 . 基于稀疏表达的微弱信号提取及检测方法[J]. 通信学报, 2015,36(Z1): 215-223.
	TANG Y F , ZHANG Y Y , GUO Z B . Approach to weak signal extraction and detection via spanse representation[J]. Journal on Communications, 2015,36(Z1): 215-223.
[14]	汤红忠, 张小刚, 陈华 ,等. 带边界条件约束的非相干字典学习方法及其稀疏表示[J]. 自动化学报, 2015,41(2): 312-319.
	TANG H Z , ZHANG X G , CHEN H ,et al. Incoherent dictionary learning method with border condition constrained for sparse representation[J]. Acta Automatic Sincia, 2015,41(2): 312-319.
[15]	TANG H Z , ZHANG X , CHEN H ,et al. Incoherent dictionary learning method based on unit norm tight frame and manifold optimization for sparse representation[J]. Mathematical Problems in Engineering, 2016.
[16]	GUREVICH S , HADANI R . Incoherent dictionaries and the statistical restricted isometry property[J]. arXiv preprint arXiv:0809.1687, 2008.
[17]	SUSTIK M A , TROPP J A , DHILLON I S ,et al. On the existence of equiangular tight frames[J]. Linear Algebra and Its Applications, 2007,426(2): 619-635.
[18]	RAKOTOMAMONJY A . Direct optimization of the dictionary learning problem[J]. IEEE Transactions on Signal Processing, 2013,61(22): 5495-5506.
[19]	LIU D C , NOCEDAL J . On the limited memory BFGS method for large scale optimization[J]. Mathematical Programming, 1989,45(1-3): 503-528.
[20]	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): 600-612.

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比较对象	图像1	图像2	图像3	图像4	图像5	图像6	平均值
标准图像	1	1	1	1	1	1	1
合成有雨图像	0.755	0.600	0.525	0.712	0.688	0.708	0.665
文献[8]算法去雨图像	0.726	0.762	0.669	0.748	0.695	0.802	0.734
文献[9]算法去雨图像	0.775	0.765	0.539	0.584	0.675	0.722	0.677
文献[10]算法去雨图像	0.752	0.805	0.635	0.821	0.700	0.741	0.742
本文算法去雨图像	0.811	0.812	0.689	0.821	0.760	0.805	0.783

比较对象	图像1	图像2	图像3	图像4	图像5	图像6	平均值
标准图像	∞	∞	∞	∞	∞	∞	∞
合成有雨图像	23.86	20.40	19.08	23.31	19.89	22.67	21.54
文献[8]算法去雨图像	24.70	25.63	19.20	25.37	22.30	21.77	23.16
文献[9]算法去雨图像	25.56	25.51	19.55	22.76	25.37	21.06	23.30
文献[10]算法去雨图像	24.55	26.14	26.70	28.71	19.50	23.10	24.78
本文算法去雨图像	26.53	26.21	25.37	26.57	19.82	24.90	24.90

图像尺寸（M×N）	文献[8]算法运行时间/s	文献[9]算法运行时间/s	文献[10]算法运行时间/s	本文算法运行时间/s
558×374	153.16	144.34	382.39	122.75
317×450	128.11	114.97	149.35	77.03
317×317	76.83	78.54	104.75	30.87

Incoherent dictionary learning and sparse representation for single-image rain removal

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