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 Rethink-Diffusion can be obtained from
this https URL

本论文提出了 Enlighten-anything 方法，结合 SAM 分割的语义意图和低光图像，采用无监督学习的零样本学习方法来改善低光图像的增强和融合，实验表明方法在 LOL 数据集上比基线提高了 3db 的峰值信噪比和 8 的结构相似度。