LoLI-Street is a low-light image enhancement dataset for training and testing low-light image enhancement models under urban street scenes.

A fork of Learning to See Moving Objects in the Dark

The project is the official implementation of our CVPR 2021 paper, "Restoring Extremely Dark Images in Real Time"

Detect and classify the vehicles in the low light using YOLO v3 pretrained model and low light image enhancement

Convolutional Denoising Autoencoder for low light image denoising

Images captured in outdoor scenes can be highly degraded due to poor lighting conditions. These images can have low dynamic ranges with high noise levels that affect the overall performance of computer vision algorithms. To make computer vision algorithms robust in low-light conditions, use low-light image enhancement to improve the visibility o…

Low light enhance for driving scene

[JON 2022] Lightweight Deep Network-Enabled Real-Time Low-Visibility Enhancement for Promoting Vessel Detection in Maritime Video Surveillance

[Access 2020] Low-Light Image Enhancement With Regularized Illumination Optimization and Deep Noise Suppression

Project of 'TBEFN: A Two-branch Exposure-fusion Network for Low-light Image Enhancement '

Pytorch implementation of Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement

LYT-Net: Lightweight YUV Transformer-based Network for Low-Light Image Enhancement

Official PyTorch code and dataset of the paper "Local Color Distributions Prior for Image Enhancement" [ECCV2022]

[ACMMM2023] "Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution", https://arxiv.org/abs/2308.01738

Image-enhancement algorithms: low-light enhancement, image restoration, super-resolution reconstruction. 图像增强算法探索：低光增强、图像修复、超分辨率重建 ……

🌕 [ICCV 2021] Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection. A self-supervised learning way for low-light image object detection.

[ECCV2022] "Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression", https://arxiv.org/abs/2207.10564

🌕 [BMVC 2022] You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction. SOTA for low light enhancement, 0.004 seconds try this for pre-processing.

Python implementation of two low-light image enhancement techniques via illumination map estimation