This is the code implementation for the paper "Improving Extreme Low-light Image Denoising via Residual Learning".
Paper: ICME (2019)
Dataset: SID Dataset
If you find this project helpful, please cite our paper.
@INPROCEEDINGS{8784972,
author={P. {Maharjan} and L. {Li} and Z. {Li} and N. {Xu} and C. {Ma} and Y. {Li}},
booktitle={2019 IEEE International Conference on Multimedia and Expo (ICME)},
title={Improving Extreme Low-Light Image Denoising via Residual Learning},
year={2019},
volume={},
number={},
pages={916-921},
keywords={computational complexity;image colour analysis;image denoising;image texture;learning (artificial intelligence);neural nets;low-light environment;low signal-to-noise ratio;deep learning based approaches;high computational cost;residual learning based deep neural network;end-to-end extreme low-light image denoising;color reproduction;texture information;Image color analysis;Image denoising;Image sensors;Sensors;Training;Task analysis;Deep learning;Deep Residual Learning;Image Denoising;Low Light Image Enhancement},
doi={10.1109/ICME.2019.00162},
ISSN={1945-7871},
month={July},}