Low-Light Image Enhancement
115 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
ClassLIE: Structure- and Illumination-Adaptive Classification for Low-Light Image Enhancement
A class prediction block is then designed to classify the degradation information by calculating the structure similarity scores on the reflectance map and mean square error on the illumination map.
ReCo-Diff: Explore Retinex-Based Condition Strategy in Diffusion Model for Low-Light Image Enhancement
Low-light image enhancement (LLIE) has achieved promising performance by employing conditional diffusion models.
DiffuseRAW: End-to-End Generative RAW Image Processing for Low-Light Images
Unlike these, we develop a new generative ISP that relies on fine-tuning latent diffusion models on RAW images and generating processed long-exposure images which allows for the apt use of the priors from large text-to-image generation models.
Learning to See Low-Light Images via Feature Domain Adaptation
To solve this problem, we propose a single-stage network empowered by Feature Domain Adaptation (FDA) to decouple the denoising and color mapping tasks in raw LLIE.
LDM-ISP: Enhancing Neural ISP for Low Light with Latent Diffusion Models
Specifically, to tailor the pre-trained latent diffusion model to operate on the RAW domain, we train a set of lightweight taming modules to inject the RAW information into the diffusion denoising process via modulating the intermediate features of UNet.
ITRE: Low-light Image Enhancement Based on Illumination Transmission Ratio Estimation
In this paper, we propose a novel Retinex-based method, called ITRE, which suppresses noise and artifacts from the origin of the model, prevents over-exposure throughout the enhancement process.
Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement
Learning-based methods have attracted a lot of research attention and led to significant improvements in low-light image enhancement.
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function
Our key insight is that ``random weight network can be acted as a constraint for training better image restoration networks''.
Training Your Image Restoration Network Better with Random Weight Network as Optimization Function
Our key insight is that ``random weight network can be acted as a constraint for training better image restoration networks''.
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.
