Low-light images suffer from low quality due to poor lighting conditions, noise pollution, and improper settings of cameras. To enhance low-light images, most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult. In contrast, a self-supervised method breaks free from the reliance on normal-light data, resulting in more convenience and better generalization. Existing self-supervised methods primarily focus on illumination adjustment and design pixel-based adjustment methods, resulting in remnants of other degradations, uneven brightness and artifacts. In response, this paper proposes a self-supervised enhancement method, termed as SLIE. It can handle multiple degradations including illumination attenuation, noise pollution, and color shift, all in a self-supervised manner. Illumination attenuation is estimated based on physical principles and local neighborhood information. The removal and correction of noise and color shift removal are solely realized with noisy images and images with color shifts. Finally, the comprehensive and fully self-supervised approach can achieve better adaptability and generalization. It is applicable to various low light conditions, and can reproduce the original color of scenes in natural light. Extensive experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art methods. Our code is available at https://github.com/hanna-xu/SLIE.

中文翻译：

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不仅仅是闪电：一种能够进行多次降级的自监督微光图像增强方法

由于光照条件差、噪声污染和相机设置不当，低光图像的质量较低。为了增强弱光图像，大多数现有方法依赖正常光图像进行引导，但收集合适的正常光图像很困难。相比之下，自监督方法摆脱了对正常光数据的依赖，从而更加方便和更好的泛化。现有的自监督方法主要关注照明调整和设计基于像素的调整方法，导致其他退化、亮度不均匀和伪影的残留。为此，本文提出了一种自监督增强方法，称为 SLIE。它可以以自我监督的方式处理多种退化，包括照明衰减、噪声污染和色偏。照度衰减是根据物理原理和当地社区信息来估计的。噪声的去除和校正以及色移去除仅针对噪声图像和具有色移的图像来实现。最后，综合性、完全自监督的方法可以实现更好的适应性和泛化性。适用于各种弱光条件，可再现自然光下场景的原始色彩。在四个公共数据集上进行的广泛实验证明了 SLIE 相对于 13 种最先进方法的优越性。我们的代码可在 https://github.com/hanna-xu/SLIE 获取。