Low-Light Image Enhancement
114 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
AFLW
3DMatch
SID
DICM
LLVIP
ExDark
MIT-Adobe FiveK
SMID
Latest papers with no code
Seeing Text in the Dark: Algorithm and Benchmark
Localizing text in low-light environments is challenging due to visual degradations.
CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement
Low-light image enhancement (LLIE) aims to improve low-illumination images.
DI-Retinex: Digital-Imaging Retinex Theory for Low-Light Image Enhancement
Many existing methods for low-light image enhancement (LLIE) based on Retinex theory ignore important factors that affect the validity of this theory in digital imaging, such as noise, quantization error, non-linearity, and dynamic range overflow.
Towards Robust Event-guided Low-Light Image Enhancement: A Large-Scale Real-World Event-Image Dataset and Novel Approach
To this end, we propose a real-world (indoor and outdoor) dataset comprising over 30K pairs of images and events under both low and normal illumination conditions.
Zero-LED: Zero-Reference Lighting Estimation Diffusion Model for Low-Light Image Enhancement
Diffusion model-based low-light image enhancement methods rely heavily on paired training data, leading to limited extensive application.
Edge-guided Low-light Image Enhancement with Inertial Bregman Alternating Linearized Minimization
To overcome this limitation, we introduce a simple yet effective Retinex model with the proposed edge extraction prior.
A Lightweight Low-Light Image Enhancement Network via Channel Prior and Gamma Correction
Human vision relies heavily on available ambient light to perceive objects.
Seed Optimization with Frozen Generator for Superior Zero-shot Low-light Enhancement
In this work, we observe that the generators, which are pre-trained on massive natural images, inherently hold the promising potential for superior low-light image enhancement against varying scenarios. Specifically, we embed a pre-trained generator to Retinex model to produce reflectance maps with enhanced detail and vividness, thereby recovering features degraded by low-light conditions. Taking one step further, we introduce a novel optimization strategy, which backpropagates the gradients to the input seeds rather than the parameters of the low-light enhancement model, thus intactly retaining the generative knowledge learned from natural images and achieving faster convergence speed.
Revealing Shadows: Low-Light Image Enhancement Using Self-Calibrated Illumination
In digital imaging, enhancing visual content in poorly lit environments is a significant challenge, as images often suffer from inadequate brightness, hidden details, and an overall reduction in quality.
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.
