TextureWGAN: Texture Preserving WGAN with MLE Regularizer for Inverse Problems

TL;DR

This paper introduces TextureWGAN, a novel approach combining Wasserstein GAN with MLE regularizer to improve texture preservation and pixel fidelity in inverse imaging problems, especially for medical imaging applications.

Contribution

The paper proposes a new WGAN-based method with MLE regularizer that better preserves image texture and fidelity compared to traditional MSE-based CNN methods.

Findings

WGAN-based method effectively preserves image texture.

The approach improves PSNR and SSIM scores.

Texture analysis confirms enhanced texture preservation.

Abstract

Many algorithms and methods have been proposed for inverse problems particularly with the recent surge of interest in machine learning and deep learning methods. Among all proposed methods, the most popular and effective method is the convolutional neural network (CNN) with mean square error (MSE). This method has been proven effective in super-resolution, image de-noising, and image reconstruction. However, this method is known to over-smooth images due to the nature of MSE. MSE based methods minimize Euclidean distance for all pixels between a baseline image and a generated image by CNN and ignore the spatial information of the pixels such as image texture. In this paper, we proposed a new method based on Wasserstein GAN (WGAN) for inverse problems. We showed that the WGAN-based method was effective to preserve image texture. It also used a maximum likelihood estimation (MLE) regularizer to preserve pixel fidelity. Maintaining image texture and pixel fidelity is the most important requirement for medical imaging. We used Peak Signal to Noise Ratio (PSNR) and Structure Similarity (SSIM) to evaluate the proposed method quantitatively. We also conducted first-order and second-order statistical image texture analysis to assess image texture.