[1] Aharon M, Elad M, and Bruckstein A, K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 2006, 54(11): 4311−4322
[2] Rey-Otero I, Sulam J, and Elad M. Variations on the convolutional sparse coding model. IEEE Transactions on Signal Processing, 2020, 68(1): 519−528
[3] Lecun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553): 436−444
[4] Bristow H, Eriksson A, Lucey S. Fast convolutional sparse coding. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013, 391−398
[5] 陈善雄, 熊海灵, 廖剑伟, 周骏, 左俊森. 一种基于 CGLS 和 LSQR 的联合优化的匹配追踪算法. 自动化学报, 2018, 44(7): 1293−1303

Chan Shan-Xiong, Xiong Hai-Ling, Liao Jian-Wei, Zhou Jun, Zuo Jun-Sen. A joint optimized matching tracking algorithm based on CGLS and LSQR. Acta Automatica Sinica, 2018, 44(7): 1293−1303
[6] Heide F, Heidrich W, Wetzstein G. Fast and flexible convolutional sparse coding. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015, 5135−5143
[7] Papyan V, Romano Y, Sulam J, Elad M. Convolutional dictionary learning via local processing. In: Proceedings of the 16th IEEE International Conference on Computer Vision (ICCV) , 2017, 5306–5314
[8] Zisselman E, Sulam J, Elad M. A local block coordinate descent algorithm for the CSC model. In: Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition, 2019, 8200−8209
[9] Papyan V, Romano Y, Elad M. Convolutional neural networks analyzed via convolutional sparse coding. The Journal of Machine Learning Research, 2017, 18(1): 2887−2938
[10] 张芳, 王萌, 肖志涛, 吴骏, 耿磊, 童军, 王雯. 基于全卷积神经网络与低秩稀疏分解的显著性检测. 自动化学报, 2019, 45(11):2148−2158

Zhang Fang, Wang Meng, Xiao Zhi-Tao, Wu Jun, Geng Lei, Tong Jun, Wang Wen. Saliency detection based on full convolutional neural network and low rank sparse decomposition.Acta Automatica Sinica, 2019, 45(11): 2148−2158
[11] Sulam J, Papyan V, Romano Y, Elad M. Multi-layer convolutional sparse modeling: Pursuit and dictionary learning. IEEE Transactions on Signal Processing, 2018, 65(15): 4090−4104
[12] Sulam J, Aberdam A, Beck A, Elad M. On multi-layer basis pursuit, efficient algorithms and convolutional neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 1968−1980
[13] Aberdam A, Sulam J, Elad M. Multi-layer sparse coding: the holistic way. SIAM Journal on Mathematics of Data Science, 2019, 1(1): 46−77
[14] 常亮, 邓小明, 周明全, 武仲科, 袁野, 杨硕, 王宏安. 图像理解中的卷积神经网络. 自动化学报, 2016, 42(9): 1300−1312

Chang Liang, Deng Xiao-Ming, Zhou Ming-Quan, Wu Zhong-Ke, Yuan Ye, Yang Shuo, Wang Hong-An. Convolution neural network in image understanding. Acta Automatica Sinica, 2016, 42(9): 1300−1312
[15] Badrinarayanan V, Kendall A, and Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481−2495
[16] Elad P, Raja G. Matching pursuit based convolutional sparse coding. In: Proceedings of the 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018, 6847−6851.
[17] Wohlberg B. Effificient algorithms for convolutional sparse representations. IEEE Transactions on Image Processing, 2016, 25(1): 301−315
[18] Sreter H, Giryes R. Learned convolutional sparse coding. In: Proceedings of the 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2018, 2191−2195
[19] Liu J, Garcia-Cardona C, Wohlberg B, Yin W. First and second order methods for online convolutional dictionary learning. SIAM Journal on Imaging Sciences, 2018: 1589−1628
[20] Garcia-Cardona C, Wohlberg B. Convolutional dictionary learning: A comparative review and new algorithms. IEEE Transactions on Computational Imaging, 2018, 4(3): 366−381
[21] Peng G J. Joint and direct optimization for dictionary learning in convolutional sparse representation. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(2):559−573
[22] Papyan V, Sulam J, and Elad M. Working locally thinking globally: Theoretical guarantees for convolutional sparse coding. IEEE Transactions on Signal Processing, 2017, 65(21): 5687−5701