Enlighten Anything: When Segment Anything Model Meets Low-Light Image Enhancement
This work addresses the problem of degraded visual information in low-light scenes for image processing applications, representing an incremental improvement by combining existing models.
The paper tackles low-light image enhancement by integrating the Segment Anything Model (SAM) with low-light images to improve visual perception, achieving a 3 dB increase in PSNR and an 8-point gain in SSIM over baseline methods on the LOL dataset.
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