WREN：基于视网膜理论的双U型网络结构的低光照图像增强
WREN: Low Light Image Enhancement Using Retinex theory-based Double U-Net-like Structures
AI总结：
研究针对低光照图像增强问题，提出基于视网膜理论的WREN神经网络，由双U型网络结构组成，通过端到端训练和尺度不变损失函数，在多数据集上取得最优性能，实现稳定的低光照图像增强。
AI中文摘要：
本文提出了一种基于视网膜理论的神经网络用于低光照图像增强，使其对各种动态范围场景都具有鲁棒性。视网膜理论是一种受人类颜色感知假设启发的图像形成模型，低光照图像被分解为固有颜色上下文（即反射率图）和场景相关照明（即照明图）。由于分解的非唯一性，现有的基于视网膜的低光照图像增强方法往往无法实现稳定分解，导致过度增强。为解决此问题，我们提出了WREN：一种具有双U型网络结构的低光照图像增强神经网络。WREN由两个类似U型网络的子网络组成。第一个网络有一个编码器和两个解码器，将输入图像分解为反射率和照明图。第二个网络在编码器和解码器之间有一个定制的Transformer块，仅增强从第一个网络获得的照明图，这完全遵循视网膜理论的假设。最后，将增强后的照明图与反射率图重新组合。该网络使用尺度不变损失函数进行端到端训练，对光照缩放具有鲁棒性。数值结果表明，我们的方法在多个数据集上取得了最优性能。我们的代码可在线获取。
英文摘要：
This paper proposes a neural network for low light image enhancement (LLIE) based on retinex theory to make LLIE robust for various dynamic range scenes. The retinex theory is an image formulation model inspired by a human color perception hypothesis, where a low light image is decomposed into intrinsic color context (i.e., reflectance map) and scene-dependent illumination (i.e., illumination map). Due to non-uniqueness of its decomposition, existing retinex-based LLIE methods often fail to achieve stable decomposition, which lead to over-enhancement. Typically, they are sensitive to the dynamic ranges that vary in different lighting conditions. To tackle this issue, we propose WREN: An LLIE neural network with double U-Net-like structures. WREN consists of two U-Net-like sub-networks. The first network has one encoder and two decoders that decompose an input image into the reflectance and illumination maps. The second network with a customized Transformer block between an encoder and a decoder only enhances the illumination map obtained from the first network: This completely follows the assumption of the retinex theory. Finally, the enhanced illumination map is recombined with the reflectance map. The network is trained end-to-end with a scale-invariant loss function, which gives robustness against the illumination scaling. Numerical results show that our method achieves the state-of-the-art performance across multiple datasets. Our code is available online.
