DURRNet: Deep Unfolded Single Image Reflection Removal Network
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by
Jun-Jie Huang, Tianrui Liu, Zhixiong Yang, Shaojing Fu, Wentao Zhao, Pier Luigi Dragotti
2022
Abstract
Single image reflection removal problem aims to divide a
reflection-contaminated image into a transmission image and a reflection image.
It is a canonical blind source separation problem and is highly ill-posed. In
this paper, we present a novel deep architecture called deep unfolded single
image reflection removal network (DURRNet) which makes an attempt to combine
the best features from model-based and learning-based paradigms and therefore
leads to a more interpretable deep architecture. Specifically, we first propose
a model-based optimization with transform-based exclusion prior and then design
an iterative algorithm with simple closed-form solutions for solving each
sub-problems. With the deep unrolling technique, we build the DURRNet with
ProxNets to model natural image priors and ProxInvNets which are constructed
with invertible networks to impose the exclusion prior. Comprehensive
experimental results on commonly used datasets demonstrate that the proposed
DURRNet achieves state-of-the-art results both visually and quantitatively.
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