时间：2023-07-18
本文（A new image decomposition approach using pixel-wise analysis sparsity model）原载Pattern Recognition，由四川大学刘怡光教授等科研人员创作，系四川大学智慧法治超前部署学科系列学术成果。后续会持续分享四川大学智慧法治超前部署学科系列学术成果，欢迎大家阅读。
Decomposing an image into two ‘simpler’ layers has been widely used in low-level vision tasks, such as image recovery and enhancement. It is an ill-posed problem since the number of unknowns are larger than the input. In this paper, a two-step strategy is introduced, including task-aware priors estimate and a decomposition model. A pixel-wise analysis sparsity model is proposed to regularize 8the separation layers, which supposes the transformed image generated with analysis operator is sparse. Unlike regularizing all pixels with one penalty weight, we try to estimate each pixel’s sparsity level with task-aware priors and to achieve pixel-wise sparse penalty. Additionally, one separation layer is regularized with both synthesis sparsity model and pixel-wise analysis sparsity model to exploit their complementary mechanisms. Unlike the analysis one utilizing image local features, the synthesis one exploits an over-complete dictionary and non-local similarity cues to provide flexible prior for regularizing the decomposition results. The proposed model is solved by an alternating optimization algorithm. We evaluate it with two applications, Retinex model and rain streaks removal. Extensive experiments on multiple enhancement datasets, many synthetic and real rainy images demonstrate that our method can remove imaging noise during Retinex decomposition, and can produce high fidelity deraining results. It achieves competing performance in terms of quantitative metrics and visual quality compared with the state-of-the-art methods.
Shuangli Du, Yiguang Liu, Minghua Zhao, Zhenyu Xu, Jie Li, Zhenzhen You. A new image decomposition approach using pixel-wise analysis sparsity model, Pattern Recognition, Pattern Recognition, Vol.136, Article 109241, April 2023..（论文下载）
