VST-Net

The Code is created based on the method described in the following paper:
M. Zhang, F. Zhang, Q. Liu, S. Wang, VST-Net: Variance-stabilizing transformation inspired network for Poisson denoising,
J. Vis. Commun. Image R., 62: 12-22, 2019.

Introduction

Inspired by the traditional VST scheme, we propose a novel Poisson denoising model based on convolutional neural network, called variance-stabilizing transform network (VST-Net). VST-Net inherits the structures and strengths of the traditional VST scheme via optimizing the nonlinear transformation by means of network design and supervised learning. The whole VST-Net network contains three sub-networks. The first and third sub-networks simulate the forward and inverse Anscombe transforms, respectively. Meanwhile, the second sub-network is devoted to playing the role of approximate Gaussian denoising. Joint learning strategy and two-stage progressive learning strategy are exploited to investigate the rationality and strength of the VST scheme.

Fig.1

The network architecture of the proposed VST-Net. It consists of three major components: SubNet1, SubNet2 and SubNet3. “C”, “B” and “R” stand for the Conv, BN and ReLU layers, respectively. For visual convenience, only one image from the 32 feature maps/images in the output of SubNet1 and SubNet2 is shown.

Fig.2

A special case of Fig. 1. The channel number of the last Conv layer at SubNet1 and SubNet2 is 1.

Fig.3

PSNR (dB) and SSIM performance of different methods on Set11 over four peaks at 0.1, 2, 10 and 30.

Requirements and Dependencies

MATLAB R2015b
Cuda-8.0

How to test

"./Demo_test_VST_Net.m" is the demo for TEST.

Paper

    @article{zhang2019vst, 
    title={VST-Net: Variance-stabilizing Transformation Inspired Network for Poisson Denoising}, 
    author={Zhang, Minghui and Zhang, Fengqin and Liu, Qiegen and Wang, Shanshan}, 
    journal={Journal of Visual Communication and Image Representation}, 
    year={2019}, 
    publisher={Elsevier}  
    }

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yqx7150 / VST-Net