Self-Reference Deep Adaptive Curve Estimation for Low-Light Image Enhancement

In this paper, we propose a 2-stage low-light image enhancement method called Self-Reference Deep Adaptive Curve Estimation (Self-DACE). In the first stage, we present an intuitive, lightweight, fast, and unsupervised luminance enhancement algorithm. The algorithm is based on a novel low-light enhancement curve that can be used to locally boost image brightness. We also propose a new loss function with a simplified physical model designed to preserve natural images' color, structure, and fidelity. We use a vanilla CNN to map each pixel through deep Adaptive Adjustment Curves (AAC) while preserving the local image structure. Secondly, we introduce the corresponding denoising scheme to remove the latent noise in the darkness. We approximately model the noise in the dark and deploy a Denoising-Net to estimate and remove the noise after the first stage. Exhaustive qualitative and quantitative analysis shows that our method outperforms existing state-of-the-art algorithms on multiple real-world datasets

Self-Aligned Concave Curve: Illumination Enhancement for Unsupervised Adaptation

Low light conditions not only degrade human visual experience, but also reduce the performance of downstream machine analytics. Although many works have been designed for low-light enhancement or domain adaptive machine analytics, the former considers less on high-level vision, while the latter neglects the potential of image-level signal adjustment. How to restore underexposed images/videos from the perspective of machine vision has long been overlooked. In this paper, we are the first to propose a learnable illumination enhancement model for high-level vision. Inspired by real camera response functions, we assume that the illumination enhancement function should be a concave curve, and propose to satisfy this concavity through discrete integral. With the intention of adapting illumination from the perspective of machine vision without task-specific annotated data, we design an asymmetric cross-domain self-supervised training strategy. Our model architecture and training designs mutually benefit each other, forming a powerful unsupervised normal-to-low light adaptation framework. Comprehensive experiments demonstrate that our method surpasses existing low-light enhancement and adaptation methods and shows superior generalization on various low-light vision tasks, including classification, detection, action recognition, and optical flow estimation. Project website: https://daooshee.github.io/SACC-Website/Comment: This paper has been accepted by ACM Multimedia 202

Weak lensing analysis of MS 1008-1224 with the VLT

We present a gravitational lensing analysis of the cluster of galaxies MS 1008-1224 (z=0.30), based on very deep observations obtained using the VLT with FORS and ISAAC during the science verification phase. We reconstructed the projected mass distribution from B,V,R,I bands using two different methods independently. The mass maps are remarkably similar, which confirm that the PSF correction worked well. The ISAAC and FORS data were combined to measure the photometric redshifts and constrain the redshift distribution of the lensed sources. The total mass inferred from weak shear is 2.3 10^{14} h^{-1} Mo on large scales, in agreement with the X-ray mass. The measured mass profile is well fit by both Navarro, Frenk and White and isothermal sphere with core radius models although the NFW is slightly better. In the inner regions, the lensing mass is about 2 times higher than the X-ray mass, which supports the view that complex physical processes in the innermost parts of clusters are responsible for the X-ray/lensing mass discrepancy. The central part of the cluster is composed of two mass peaks whose the center of mass is 15 arcsecond north of the cD galaxy. This provides an explanation for the 15 arcsecond offset between the cD and the center of the X-ray map reported elsewhere. The optical, X-ray and the mass distributions show that MS 1008-1224 is composed of many subsystems which are probably undergoing a merger. MS 1008-1224 shows a remarkable case of cluster-cluster lensing. The photometric redshifts show an excess of galaxies located 30 arcseconds south-west of the cD galaxy at a redshift of about 0.9 which is lensed by MS 1008-1224. These results show the importance of getting BVRIJK images silmultenously. The VLT is a unique tool to provide such datasets.Comment: 22 pages, submitted to A&A, paper with `big' figures available at ftp://ftp.cita.utoronto.ca/pub/waerbeke/ms1008paper

Enlighten-anything:When Segment Anything Model Meets Low-light Image Enhancement

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 https://github.com/zhangbaijin/enlighten-anythin

KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image

Low-light images often suffer from noise and color distortion. Object detection, semantic segmentation, instance segmentation, and other tasks are challenging when working with low-light images because of image noise and chromatic aberration. We also found that the conventional Retinex theory loses information in adjusting the image for low-light tasks. In response to the aforementioned problem, this paper proposes an algorithm for low illumination enhancement. The proposed method, KinD-LCE, uses a light curve estimation module to enhance the illumination map in the Retinex decomposed image, improving the overall image brightness. An illumination map and reflection map fusion module were also proposed to restore the image details and reduce detail loss. Additionally, a TV(total variation) loss function was applied to eliminate noise. Our method was trained on the GladNet dataset, known for its diverse collection of low-light images, tested against the Low-Light dataset, and evaluated using the ExDark dataset for downstream tasks, demonstrating competitive performance with a PSNR of 19.7216 and SSIM of 0.8213.Comment: Accepted by Signal, Image and Video Processin