Projects sharing features with LLVIP

This project is a deep learning computer vision implementation focused on low-light image restoration. It uses a neural network to process raw sensor data, mapping underexposed images to well-exposed versions to improve visibility and restore natural colors. The implementation is based on CVPR 2018 research and utilizes TensorFlow to execute the computational graph. It employs a convolutional neural network and pixel-wise regression to reconstruct scene lighting directly from unprocessed raw image data. The project includes a framework for supervised pair learning, where models are trained u

This repo is the official implementation of ICCV2021 paper "Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement". For more details, please see our paper.

2023.12.15: Our new work Aleth-NeRF: Illumination Adaptive NeRF with Concealing Field Assumption has been accepted by AAAI 2024, please refer (here) if you interest in NeRF under low-light~

Siggraph 2017

FG 2018 FOR-LQ workshop GLADNet

this is the official code for the paper "Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement"

This is a pytorch project for the paper SNR-aware Low-Light Image Enhancement by Xiaogang Xu, Ruixing Wang, Chi-Wing Fu, and Jiaya Jia presented at CVPR2022.

[Paper](https://dl.acm.org/doi/abs/10.1145/3503161.3548135)

Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang and Jiaying Liu

By Chu Zhou, Minggui Teng, Youwei Lyu, Si Li, Chao Xu, Boxin Shi

ICLR2025 Spotlight ✨

See URetinex-Net++. We release training code of URetinex-Net++, which share the decomposition module and unfolding module with URetinex-Net, enjoy!

HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset (ICCV 2021) Guanying Chen, Chaofeng Chen, Shi Guo, Zhetong Liang, Kwan-Yee K. Wong, Lei Zhang

The implementation of CVPR 2023 paper "Physics-Guided ISO-Dependent Sensor Noise Modeling for Extreme Low-Light Photography" Paper" and its supplementary materials "supplementary materials".

Corruption and Perturbation Robustness (ICLR 2019)

Without denoise: Self-supervised Image Enhancement Network: Training With Low Light Images Only paper

Learning Image-adaptive 3D Lookup Tables for High Performance Photo Enhancement in Real-time

Codes for "A Dual Sensor Computational Camera for High Quality Dark Videography"

Raman Jha, Adithya Lenka, Mani Ramanagopal, Aswin Sankaranarayanan, Kaushik Mitra

1.Sichuan University, 2.Megvii Technology, 3.University of Electronic Science and Technology of China

This project is a community-driven academic resource index and knowledge base dedicated to the study of transfer learning and domain adaptation. It functions as a curated repository of scholarly materials, including academic papers, tutorials, datasets, and benchmarks, designed to support research into how machine learning models apply knowledge from one task to another. The repository organizes these resources into a hierarchical taxonomy to facilitate the discovery of specialized methodologies. By leveraging distributed version control, the project maintains an evolving archive of research

This is an implementation of the following paper.

Jie Huang+, Yajing Liu+, Feng Zhao*, Keyu Yan, Jinghao Zhang, Yukun Huang, Man Zhou, Zhiwei Xiong

This repository contains the official PyTorch implementation of EFINet, a lightweight enhancement-fusion iterative network for restoring low-light images.

By Cheuk-Yiu Chan, Wan-Chi Siu, Yuk-Hee Chan and H. Anthony Chan