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论文类型：期刊论文

发表时间：2019-02-01

发表刊物：IEEE SIGNAL PROCESSING LETTERS

收录刊物：SCIE、EI

卷号：26

期号：2

页面范围：307-311

ISSN号：1070-9908

关键字：Learning-based optimization; rain streaks removal; deep unrolling; bilevel layer priors

摘要：Rain streaks removal is an important issue of the outdoor vision system and recently has been investigated extensively. In the past decades, maximum a posterior and network-based architecture have been attracting considerable attention for this problem. However, it is challenging to establish effective regularization priors and the cost function with complex prior is hard to optimize. On the other hand, it is still hard to incorporate data-dependent information into conventional numerical iterations. To partially address the above limits and inspired by the leader- follower gaming perspective, we introduce an unrolling strategy to incorporate data-dependent network architectures into the established iterations, i.e., a learning bilevel layer priors method to jointly investigate the learnable feasibility and optimality of rain streaks removal problem. Both visual and quantitative comparison results demonstrate that our method outperforms the state of the art.