论文标题
SID-NISM：一个自制的低光图像增强框架
SID-NISM: A Self-supervised Low-light Image Enhancement Framework
论文作者
论文摘要
当在弱光条件下捕获图像时，这些图像通常会遭受较低的可见性，这不仅会降低图像的视觉美学，而且还显着退化了许多计算机视觉算法的性能。在本文中，我们提出了一个自我监督的低光图像增强框架（SID-Nism），该框架由两个组件组成，一个自我监督的图像分解网络（SID-NET）和非线性照明饱和映射映射功能（NISM）。作为一个自我监督的网络，SID-NET可以直接将给定的低光图像分解为其反射率，照明和噪声，而无需任何先前的培训或参考图像，这将其与现有的监督学习方法区分开来。然后，分解的照明图将通过NISM增强。拥有恢复的照明图，可以相应地实现增强。在几个公共挑战的低光图像数据集中进行的实验表明，通过SID-NISM增强的图像更自然，并且具有较少意外的伪影。
When capturing images in low-light conditions, the images often suffer from low visibility, which not only degrades the visual aesthetics of images, but also significantly degenerates the performance of many computer vision algorithms. In this paper, we propose a self-supervised low-light image enhancement framework (SID-NISM), which consists of two components, a Self-supervised Image Decomposition Network (SID-Net) and a Nonlinear Illumination Saturation Mapping function (NISM). As a self-supervised network, SID-Net could decompose the given low-light image into its reflectance, illumination and noise directly without any prior training or reference image, which distinguishes it from existing supervised-learning methods greatly. Then, the decomposed illumination map will be enhanced by NISM. Having the restored illumination map, the enhancement can be achieved accordingly. Experiments on several public challenging low-light image datasets reveal that the images enhanced by SID-NISM are more natural and have less unexpected artifacts.
