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Indexed by:Journal Papers

Date of Publication:2019-02-01

Journal:IEEE SIGNAL PROCESSING LETTERS

Included Journals:SCIE、EI

Volume:26

Issue:2

Page Number:307-311

ISSN No.:1070-9908

Key Words:Learning-based optimization; rain streaks removal; deep unrolling; bilevel layer priors

Abstract: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.