论文标题
通过合作的先验体系结构搜索低光图像增强的启发，以视网膜启发的展开
Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement
论文作者
论文摘要
低光图像增强在低级视觉场中起着非常重要的作用。最近的作品已经建立了各种深度学习模型来解决这一任务。但是，这些方法主要依赖于重要的建筑工程，并且遭受了很高的计算负担。在本文中，我们提出了一种新方法，称为“架构搜索”（RUAS），称为Etinex启发的展开（RUAS），以在现实世界中为低光图像构建轻巧但有效的增强网络。具体而言，RUAS在基于Itinex规则的基础上，首先建立了模型来表征弱光图像的内在未充满刺激的结构，并展开其优化过程以构建我们的整体传播结构。然后，通过设计一种合作的无参考学习策略来从紧凑的搜索空间中发现较低的先前体系结构，RUAS能够获得表现最佳的图像增强网络，该网络具有快速的速度，需要很少的计算资源。广泛的实验验证了我们RUAS框架与最近提出的最新方法的优越性。
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.
