结合自适应稀疏表示和全变分约束的图像重建

doi:10.3969/j.issn.1001-2400.2016.01.003

Abstract

Abstract:

In view of the limitation of fixed complete orthogonal transformation, represented by two-dimensional wavelet transform and discrete cosine transform in compressed sensing high-resolution image reconstruction, this paper proposes a new method for high-resolution image reconstruction based on adaptive redundant dictionary sparse representation with the total variation constraint.The algorithm takes the intermediate image in the process of iteration as the training sample to get a redundant dictionary suitable for sample characteristics by adaptive learning. It makes full use of the correlation between dictionary atoms and the image to get an ideal complete sparse representation, thus reducing the sampling rate and improving the quality of image reconstruction. Finally, the algorithm takes the total variation as a constraint and uses the split Bregman iterative method to solve the sparse optimization problem. Simulation shows that the proposed method can reconstruct high quality images under a low sampling rate.

Key words: compressed sensing, adaptive redundant dictionary, sparse representation, image reconstruction, total variation

WANG Yong;FENG Tangzhi;CHEN Chuchu;QIAO Qianqian;YANG Xiaoyu;WANG Guodong. Adaptive sparse representation and total variation constraint based image reconstruction[J].J4, 2016, 43(1): 12-17+109.

References

［1］ DONOHO D L. Compressed Sensing［J］. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
［2］ ZHAN X, ZHANG R, YIN D, et al. SAR Image Compression Using Multiscale Dictionary Learning and Sparse Representation［J］. IEEE Geoscience and Remote Sensing Letters, 2013,10 (5): 1090-1094.
［3］ MA L Y, MOISAN L, YU J, et al. A Dictionary Learning Approach for Poisson Image Deblurring［J］. IEEE Transactions on Medical Imaging, 2013, 32(7): 1277-1289.
［4］王良君, 石光明, 李甫, 等. 多稀疏空间下的压缩感知图像重构［J］. 西安电子科技大学学报, 2013, 40(3): 73-80.
WANG Liangjun, SHI Guangming, LI Fu, et al. Compressed Sensing Image Reconstruction in Multiple Sparse Spaces［J］. Journal of Xidian University, 2013, 40(3): 73-80.
［5］ HUANG H J, YU J, SUN W D. Superresolution Mapping Using Multiple Dictionaries by Sparse Representation［J］. IEEE Geoscience and Remote Sensing Letters, 2014, 11(12): 2055-2059.
［6］ HUANG D A, KANG L W, CHIANG Y, et al. Self-learning Based Image Decomposition with Applications to Single Image Denoising ［J］. IEEE Transactions on Multimedia, 2014, 16(1): 83-93.
［7］ YANG M, ZHANG L, FENG X C, et al. Sparse Representation Based Fisher Discrimination Dictionary Learning for Image Classification［J］. International Journal of Computer Vision, 2014, 109(3): 209-232.
［8］许建楼, 冯象初, 郝岩. 二阶总广义变分图像修复模型及其算法［J］. 西安电子科技大学学报, 2012, 39(5): 18-23.
XU Jianlou, FENG Xiangchu, HAO Yan. Second Order Total Generalized Variational Inpainting model and Its Algorithm［J］. Journal of Xidian University, 2012, 39(5): 18-23.
［9］练秋生, 周婷. 结合字典稀疏表示和非局部相似性的自适应压缩成像算法［J］. 电子学报, 2012, 40(7): 1416-1422.
LIAN Qiusheng, ZHOU Ting. Adaptive Compressed Imaging Algorithm Combined the Sparse Representation in the Dictionaries with Non-Local Similarity［J］. Acta Electronica Sinica, 2012, 40(7): 1416-1422.
［10］ DONG W, ZHANG L, SHI G, et al. Nonlocally Centralized Sparse Representation for Image Restoration［J］. IEEE Transactions on Image Processing, 2013, 22(4): 1620-1630.
［11］ AHARON M, ELAD M, BRUCKSTEIN A. K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation［J］. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322.
［12］ GOLDSTEIN T, OSHER S. The Split Bregman method for L1 regularized problems［J］. SIAM Journal of Imaging Sciences, 2009, 2(2): 323-343.
［13］ YIN W, OSHER S, GOLDFARB D, et al. Bragman Iterative Algorithms for L1-Regularization with Application to Compressed Sensing［J］. SIAM Journal of Imaging Sciences, 2008, 1(1): 143-168.
［14］ FIGUEIREDO M A T, NOUAK 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, 2007, 1(4): 586-597.
［15］ FOWLER J E, MUN S, TRAMEL E W. Multiscale Block Compressed Sensing with Smoothed Projected Landweber Reconstruction［C］//European Signal Processing Conference. Poland: EUSIPCO, 2011: 564-568.
［16］ JI S H, XUE Y, CARIN L. Bayesian Compressive Sensing ［J］. IEEE Transactions on Signal Processing, 2008, 56(6): 2346-2356.

[1]	LU Yan,LIAO Guisheng,HUANG Qingxiang. Cross-camera moving target tracking algorithm based on sparse representation [J]. Journal of Xidian University, 2021, 48(2): 197-204.
[2]	LI Kunlun,ZHANG Lu,XU Hongke,SONG Huansheng. Waveletdomain dilated network for fast low-dose CT image reconstruction [J]. Journal of Xidian University, 2020, 47(4): 86-93.
[3]	ZHAO Hui,ZHANG Le,LIU Yingli,ZHANG Jing,ZHANG Tianqi. Optimization of the low coherence and high robustness observation matrix [J]. Journal of Xidian University, 2019, 46(6): 171-178.
[4]	HE Wangpeng,HU Jie,CHEN Binqiang,LI Cheng,GUO Baolong. Sparsity-induced resonance demodulation method for blade crack detection [J]. Journal of Xidian University, 2019, 46(6): 75-80.
[5]	YANG Lei,YUE Yunze,LI Pucheng,ZHANG Tao,YANG Huan. Sparse representation of large dynamic range SAR imaging for multiple ground moving targets [J]. Journal of Xidian University, 2019, 46(5): 31-40.
[6]	WANG Hongyan,QIU Helei,PEI Tengda. Visual tracking method using discriminant dictionary learning [J]. Journal of Xidian University, 2019, 46(4): 150-158.
[7]	ZHOU Xueqin,FENG Xiangchu,JING Mingli. Adaptive quasi-newton projection algorithm for sparse recovery [J]. Journal of Xidian University, 2019, 46(3): 14-19.
[8]	JIA Hairong,WANG Weimei,WANG Yan,PEI Junhua. Speech enhancement based on discriminative joint sparse dictionary alternate optimization [J]. Journal of Xidian University, 2019, 46(3): 74-81.
[9]	ZENG Chuangzhan,ZHU Weigang,JIA Xin. Sparse-aperture ISAR imaging algorithm [J]. Journal of Xidian University, 2019, 46(3): 123-129.
[10]	YU Lihong,ZHAO Jiaxiang. Design of sparse MIMO equalizers using least angle regression [J]. Journal of Xidian University, 2019, 46(1): 73-78.
[11]	WANG Chuanchuan,ZENG Yonghu,FU Weihong,WANG Liandong. Estimation algorithm for an underdetermined mixing matrix based on maximum density point searching [J]. Journal of Xidian University, 2019, 46(1): 106-111.
[12]	LI Long;LIU Zheng. Noise-robust multi-feature joint learning HRRP recognition method [J]. Journal of Xidian University, 2018, 45(4): 57-62.
[13]	LV Zhiguo;LI Ying. Compressed sensing-based Bayesian channel estimation algorithm [J]. Journal of Xidian University, 2018, 45(2): 13-18+25.
[14]	HOU Yuqing;WEI Hongna;YI Huangjian;ZHANG Xu;HE Xiaowei. Imaging system of fluorescence molecular tomography with spiral excitation [J]. Journal of Xidian University, 2018, 45(2): 97-102.
[15]	WANG Shuzhen;XIE Kun;LI Li;ZHANG Jingang. Improved RLTV algorithm used to radio image reconstruction [J]. Journal of Xidian University, 2017, 44(3): 72-76.

Adaptive sparse representation and total variation constraint based image reconstruction

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 10