low-light-enhancement

"Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement" (ICCV 2023 Top-10 Cited 🏆) & (NTIRE 2024 Runner-Up 🏆) & (NTIRE 2025 Winner 🏆) & (NTIRE 2026 Winner 🏆)

ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration

[CVPR 2026] Multinex: Lightweight Low-light Image Enhancement via Multi-prior Retinex

[MICCAI 2024 Best Paper Honorable Mention] LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with T-Diffusion

StarIR: Convolutional Image Restoration with Spatial-Frequency Fusion

[OCMA] Deep learning-driven surveillance quality enhancement for maritime management promotion under low-visibility weathers

PixelSpark is a professional AI photo editor for Android that enhances low-light images using MIRNet TFLite 100% offline, no cloud required. Built with Flutter and a high-performance C++ core via dart:ffi, it delivers 21+ pro editing tools, non-destructive workflow, and a smooth white/sky-blue UI. Features: AI Enhancement

Low-light image and video enhancement model for Jetson-class edge deployment.

Enhances photo quality using AI upscaling — a small Windows tool that does it right

CSE468 Computer Vision Project | Low-light CCTV surveillance system using YOLOv8, MTCNN & OpenCV for human, face, and vehicle detection in nighttime conditions. Supervised by Dr. Mohammad Shifat-E-Rabbi, North South University.

A comparison of classical image processing vs. the Zero-DCE++ deep learning model for unsupervised low-light image enhancement using PyTorch

EAIM-Net: Unified image enhancement for scene text — low-light, haze, rain, glare, illumination

Unofficial PyTorch reproduction for URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image Restoration.

Research-based implementation and enhancement of the AGCC framework for low-light image enhancement. Includes reproduction of a 2024 research paper, evaluation using PSNR, SSIM, NIQE, and BRISQUE, and an improved hybrid model using CLAHE, Bilateral Filtering, Wavelet Denoising, Canny Edge Detection, and Morphological Operations.