Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77671
Title: Joint convolutional analysis and synthesis sparse representation for single image layer separation
Authors: Gu, S 
Meng, D
Zuo, W
Zhang, L 
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: Proceedings of the IEEE International Conference on Computer Vision, 2017, 22-29 Oct. 2017, 8237451, p. 1717-1725 How to cite?
Abstract: Analysis sparse representation (ASR) and synthesis sparse representation (SSR) are two representative approaches for sparsity-based image modeling. An image is described mainly by the non-zero coefficients in SSR, while is mainly characterized by the indices of zeros in ASR. To exploit the complementary representation mechanisms of ASR and SSR, we integrate the two models and propose a joint convolutional analysis and synthesis (JCAS) sparse representation model. The convolutional implementation is adopted to more effectively exploit the image global information. In JCAS, a single image is decomposed into two layers, one is approximated by ASR to represent image large-scale structures, and the other by SSR to represent image fine-scale textures. The synthesis dictionary is adaptively learned in JCAS to describe the texture patterns for different single image layer separation tasks. We evaluate the proposed JCAS model on a variety of applications, including rain streak removal, high dynamic range image tone mapping, etc. The results show that our JCAS method outperforms state-of-the-arts in these applications in terms of both quantitative measure and visual perception quality.
URI: http://hdl.handle.net/10397/77671
ISBN: 9.78154E+12
DOI: 10.1109/ICCV.2017.189
Appears in Collections:Conference Paper

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