Low-Light Image Enhancement
118 papers with code • 21 benchmarks • 21 datasets
Low-Light Image Enhancement is a computer vision task that involves improving the quality of images captured under low-light conditions. The goal of low-light image enhancement is to make images brighter, clearer, and more visually appealing, without introducing too much noise or distortion.
Benchmarks
These leaderboards are used to track progress in Low-Light Image Enhancement
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Libraries
Use these libraries to find Low-Light Image Enhancement models and implementations
LOL
3DMatch
AFLW
SID
DICM
LLVIP
ExDark
MIT-Adobe FiveK
SMID
Latest papers with no code
DEFormer: DCT-driven Enhancement Transformer for Low-light Image and Dark Vision
However, it is difficult to restore the lost details in the dark area by relying only on the RGB domain.
Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model
Therefore, Diff-Retinex formulates the low-light image enhancement problem into Retinex decomposition and conditional image generation.
DiffLLE: Diffusion-guided Domain Calibration for Unsupervised Low-light Image Enhancement
Specifically, we adopt a naive unsupervised enhancement algorithm to realize preliminary restoration and design two zero-shot plug-and-play modules based on diffusion model to improve generalization and effectiveness.
Low-Light Image Enhancement with Illumination-Aware Gamma Correction and Complete Image Modelling Network
We propose to integrate the effectiveness of gamma correction with the strong modelling capacities of deep networks, which enables the correction factor gamma to be learned in a coarse to elaborate manner via adaptively perceiving the deviated illumination.
CLE Diffusion: Controllable Light Enhancement Diffusion Model
Low light enhancement has gained increasing importance with the rapid development of visual creation and editing.
Brighten-and-Colorize: A Decoupled Network for Customized Low-Light Image Enhancement
The colorization sub-task is accomplished by regarding the chrominance of the low-light image as color guidance like the user-guide image colorization.
Dual Degradation-Inspired Deep Unfolding Network for Low-Light Image Enhancement
Although low-light image enhancement has achieved great stride based on deep enhancement models, most of them mainly stress on enhancement performance via an elaborated black-box network and rarely explore the physical significance of enhancement models.
Decomposition Ascribed Synergistic Learning for Unified Image Restoration
Learning to restore multiple image degradations within a single model is quite beneficial for real-world applications.
From Generation to Suppression: Towards Effective Irregular Glow Removal for Nighttime Visibility Enhancement
Most existing Low-Light Image Enhancement (LLIE) methods are primarily designed to improve brightness in dark regions, which suffer from severe degradation in nighttime images.
Division Gets Better: Learning Brightness-Aware and Detail-Sensitive Representations for Low-Light Image Enhancement
Low-light image enhancement strives to improve the contrast, adjust the visibility, and restore the distortion in color and texture.
