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论文类型：期刊论文

发表时间：2020-05-01

发表刊物：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录刊物：SCIE

卷号：31

期号：5

页面范围：1653-1666

ISSN号：2162-237X

关键字：Task analysis; Rain; Deep learning; Image edge detection; Lighting; Learning systems; Visualization; Deep unrolling; image enhancement; knowledge-driven; single-image layer separation

摘要：Single-image layer separation targets to decompose the observed image into two independent components in terms of different application demands. It is known that many vision and multimedia applications can be (re)formulated as a separation problem. Due to the fundamentally ill-posed natural of these separations, existing methods are inclined to investigate model priors on the separated components elaborately. Nevertheless, it is knotty to optimize the cost function with complicated model regularizations. Effectiveness is greatly conceded by the settled iteration mechanism, and the adaption cannot be guaranteed due to the poor data fitting. What is more, for a universal framework, the most taxing point is that one type of visual cue cannot be shared with different tasks. To partly overcome the weaknesses mentioned earlier, we delve into a generic optimization unrolling technique to incorporate deep architectures into iterations for adaptive image layer separation. First, we propose a general energy model with implicit priors, which is based on maximum a posterior, and employ the extensively accepted alternating direction method of multiplier to determine our elementary iteration mechanism. By unrolling with one general residual architecture prior and one task-specific prior, we attain a straightforward, flexible, and data-dependent image separation framework successfully. We apply our method to four different tasks, including single-image-rain streak removal, high-dynamic-range tone mapping, low-light image enhancement, and single-image reflection removal. Extensive experiments demonstrate that the proposed method is applicable to multiple tasks and outperforms the state of the arts by a large margin qualitatively and quantitatively.