Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement

• URL: http://arxiv.org/abs/2012.05609v1
• Date: Thu, 10 Dec 2020 11:51:23 GMT
• Title: Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement
• Authors: Risheng Liu and Long Ma and Jiaao Zhang and Xin Fan and Zhongxuan Luo
• Abstract summary: We propose Retinex-inspired Unrolling with Architecture Search (RUAS) to construct lightweight yet effective enhancement network for low-light images. RUAS is able to obtain a top-performing image enhancement network, which is with fast speed and requires few computational resources.
• Score: 58.72667941107544
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-light image enhancement plays very important roles in low-level vision field. Recent works have built a large variety of deep learning models to address this task. However, these approaches mostly rely on significant architecture engineering and suffer from high computational burden. In this paper, we propose a new method, named Retinex-inspired Unrolling with Architecture Search (RUAS), to construct lightweight yet effective enhancement network for low-light images in real-world scenario. Specifically, building upon Retinex rule, RUAS first establishes models to characterize the intrinsic underexposed structure of low-light images and unroll their optimization processes to construct our holistic propagation structure. Then by designing a cooperative reference-free learning strategy to discover low-light prior architectures from a compact search space, RUAS is able to obtain a top-performing image enhancement network, which is with fast speed and requires few computational resources. Extensive experiments verify the superiority of our RUAS framework against recently proposed state-of-the-art methods.

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