This repository provides codes that we use to study the mutual influence between image denoising and high-level vision tasks.
(1) We present an image denoising network which achieves state-of-the-art image denoising performance.
(2) We propose a deep network solution that cascades two modules for image denoising and various high-level tasks, respectively, and demonstrate that the proposed architecture not only yields superior image denoising results preserving fine details, but also overcomes the performance degradation of different high-level vision tasks, such as image classification and semantic segmentation, due to image noise.
This code repository is built on top of DeepLab v2.
For more details, please refer to our paper.
cd exper/model/- Run
get_models.shto download models used in our work.
cd exper- Run
main_train_denoise.shto train the denoising network seperately. - Run
main_train_cls.shto jointly train the cascade of the denoising network and the network for image classification. - Run
main_train_seg.shto jointly train the cascade of the denoising network and the entwork for semantic segmentation.
cd exper- Run
main_test_cls.shto test the resulting model for image classification. - Run
main_test_seg.shto test the resulting model for semantic segmentation. - Run
main_test_denoise.shto generate denoised results.
Please cite the paper in your publications if it helps your research:
@inproceedings{liu2017when,
author = {Liu, Ding and Wen, Bihan and Liu, Xianming and Huang, Thomas S.},
title = {When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach},
year = {2017},
journal={arXiv preprint arXiv:1706.04284}
}