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Found 5 papers, 5 papers with code
LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models
In this paper, we propose a diffusion-based unsupervised framework that incorporates physically explainable Retinex theory with diffusion models for low-light image enhancement, named LightenDiffusion.
Supervised Homography Learning with Realistic Dataset Generation
In this paper, we propose an iterative framework, which consists of two phases: a generation phase and a training phase, to generate realistic training data and yield a supervised homography network.
Low-Light Image Enhancement with Wavelet-based Diffusion Models
Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration.
Ranked #2 on
Low-Light Image Enhancement
on LOLv2
Semi-supervised Deep Large-baseline Homography Estimation with Progressive Equivalence Constraint
Homography estimation is erroneous in the case of large-baseline due to the low image overlay and limited receptive field.
R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network
Solving a series of degradation of low-light images can effectively improve the visual quality of images and the performance of high-level visual tasks.
