论文标题
TextureWgan：质地保留WGAN的使用MLE正常制度，以解决反问题
TextureWGAN: Texture Preserving WGAN with MLE Regularizer for Inverse Problems
论文作者
论文摘要
已经提出了许多算法和方法针对反问题，尤其是在最近对机器学习和深度学习方法的兴趣激增的情况下。在所有提出的方法中，最流行和有效的方法是具有均方根误差（MSE）的卷积神经网络（CNN）。该方法已被证明在超分辨率，图像去噪声和图像重建方面有效。但是，由于MSE的性质，已知该方法是过度光滑图像的。基于MSE的方法将基线图像和CNN生成图像之间所有像素的欧几里得距离最小化，而忽略像素（例如图像纹理）的空间信息。在本文中，我们提出了一种基于Wasserstein Gan（WGAN）的新方法，以解决反问题。我们表明，基于木材的方法可有效保留图像纹理。它还使用了最大似然估计（MLE）正常器来保留像素保真度。保持图像纹理和像素保真度是医学成像的最重要要求。我们使用峰信号与噪声比（PSNR）和结构相似性（SSIM）来定量评估所提出的方法。我们还进行了一阶和二阶统计图像纹理分析，以评估图像纹理。
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.
