Image captured under low-light conditions presents unpleasing artifacts, which debilitate the performance of feature extraction for many upstream visual tasks. Low-light image enhancement aims at improving brightness and contrast, and further reducing noise that corrupts the visual quality. Recently, many image restoration methods based on Swin Transformer have been proposed and achieve impressive performance. However, on one hand, trivially employing Swin Transformer for low-light image enhancement would expose some artifacts, including over-exposure, brightness imbalance and noise corruption, etc. On the other hand, it is impractical to capture image pairs of low-light images and corresponding ground-truth, i.e. well-exposed image in same visual scene. In this paper, we propose a dual-branch network based on Swin Transformer, guided by a signal-to-noise ratio prior map which provides the spatial-varying information for low-light image enhancement. Moreover, we leverage unsupervised learning to construct the optimization objective based on Retinex model, to guide the training of proposed network. Experimental results demonstrate that the proposed model is competitive with the baseline models.

翻译：低光照条件下捕获的图像会产生令人不悦的伪影，这削弱了众多上游视觉任务的特征提取性能。低光照图像增强旨在提升亮度和对比度，并进一步减少破坏视觉质量的噪声。近年来，基于Swin Transformer的多种图像复原方法被提出并取得了显著性能。然而，一方面，直接在低光照图像增强中应用Swin Transformer会引入过曝、亮度不均和噪声污染等伪影；另一方面，捕获低光照图像与对应真实标准图像（即同一视觉场景下的正常曝光图像）的配对数据并不现实。本文提出一种基于Swin Transformer的双分支网络，其由信噪比先验图引导，该先验图为低光照图像增强提供空间变化信息。此外，我们利用无监督学习基于Retinex模型构建优化目标，以指导所提网络的训练。实验结果表明，所提模型与基线模型相比具有竞争力。