Awesome

Segmentation-aware Image Denoising Without Knowing True Segmentation

Implement of the paper: Segmentation-aware Image Denoising Without Knowing True Segmentation Sicheng Wang, Bihan Wen, Junru Wu, Dacheng Tao, Zhangyang Wang

Overview

we propose a segmentation-aware image denoising model dubbed U-SAID, which does not need any ground-truth segmentation map in training, and thus can be applied to any image dataset directly. We demonstrate the U-SAID generates denoised image has:

better visual quality;
stronger robustness for subsequent semantic segmentation tasks.

We also manifest U-SAID's superior generalizability in three folds:

denoising unseen types of images;
pre-processing unseen noisy images for segmentation;
pre-processing unseen images for unseen high-level tasks.

Methods

U-SAID: Network architecture. The USA module is composed of a feature embedding sub-network for transforming the denoised image to a feature space, followed by an unsupervised segmentation sub-network that projects the feature to a segmentation map and calculates its pixel-wise uncertainty.

Visual Examples

Visual comparison on Kodak Images

Semantic segmentation from Pascal VOC 2012 validation set

How to run

Dependences

PyTorch
torchvision
OpenCV for Python
tensorboardX (TensorBoard for PyTorch)

Train

USAID_train.py

Saved Models

Saved_Models/USAID.pth

Citation

If you use this code for your research, please cite our paper.

@misc{1905.08965,
Author = {Sicheng Wang and Bihan Wen and Junru Wu and Dacheng Tao and Zhangyang Wang},
Title = {Segmentation-Aware Image Denoising without Knowing True Segmentation},
Year = {2019},
Eprint = {arXiv:1905.08965},
}