Image restoration is a low-level visual task, and most CNN methods are designed as black boxes, lacking transparency and intrinsic aesthetics. Many unsupervised approaches ignore the degradation of visible information in low-light scenes, which will seriously affect the aggregation of complementary information and also make the fusion algorithm unable to produce satisfactory fusion results under extreme conditions. In this paper, we propose Enlighten-anything, which is able to enhance and fuse the semantic intent of SAM segmentation with low-light images to obtain fused images with good visual perception. The generalization ability of unsupervised learning is greatly improved, and experiments on LOL dataset are conducted to show that our method improves 3db in PSNR over baseline and 8 in SSIM. Zero-shot learning of SAM introduces a powerful aid for unsupervised low-light enhancement. The source code of Enlighten Anything can be obtained from https://github.com/zhangbaijin/enlighten-anything

翻译：图像恢复是一项低级视觉任务，大多数卷积神经网络方法被设计为黑箱，缺乏透明性和内在美学。许多无监督方法忽略了低光照场景中可见信息的退化，这将严重影响互补信息的聚合，并使融合算法在极端条件下无法产生令人满意的融合结果。本文提出“Enlighten Anything”方法，能够将SAM（分割一切模型）分割的语义意图与低光照图像进行增强与融合，从而获得具有良好视觉感知的融合图像。无监督学习的泛化能力得到大幅提升，在LOL数据集上的实验表明，我们方法相较于基线在PSNR上提升3dB，在SSIM上提升8。SAM的零样本学习为无监督低光照增强引入强大辅助。Enlighten Anything的源代码可从https://github.com/zhangbaijin/enlighten-anything获取。