In this paper, we propose a variational autoencoder (VAE) and a VAE-generative adversarial net (GAN) trained to generate from 12000 Ising granularity images, new and appropriate images, which can retain the former\({}'s\) global chaotic structure to some extent. Via VAE, we project high-dimensional Ising granularity images onto a two-dimensional latent space in which some spatial distribution patterns are explored. The observed particles in latent space electronic cloud are similar to that of the quantum dynamics integrable pattern. The resulting VAE latent space is a new measurement space to explore both the spatial particle distribution patterns and the structural topology clusters, leading to recognition of new classification/clustering patterns of the physical state/phase, which extend those found via traditional approaches which consider pixels of an image as physical particles. In addition, we propose a multiple-level structural similarity image quality assessment (IQA) scheme to measure inter- and intra-patch similarities on VAE and VAE–GAN generate images when they are split into patches. The results show that this novel IQA scheme can both maximize the distances of the samples among inter-classes and minimize those of the intra-classes, without compromising the image fidelity and features.

在本文中，我们提出了一个变分自动编码器 (VAE) 和一个 VAE 生成对抗网络 (GAN)，经过训练，可以从 12000 个伊辛粒度图像中生成新的和合适的图像，可以保留之前的\({}'s\)全球混沌结构在一定程度上。通过 VAE，我们将高维 Ising 粒度图像投影到二维潜在空间中，在该空间中探索了一些空间分布模式。在潜空间电子云中观察到的粒子类似于量子动力学可积模式。由此产生的 VAE 潜在空间是一个新的测量空间，用于探索空间粒子分布模式和结构拓扑集群，从而识别物理状态/阶段的新分类/集群模式，这扩展了通过考虑像素的传统方法发现的模式图像作为物理粒子。此外，我们提出了一种多级结构相似性图像质量评估 (IQA) 方案来测量 VAE 上的块间和块内相似性，并且 VAE-GAN 在将图像分割成块时生成图像。结果表明，这种新颖的 IQA 方案既可以最大化类间样本的距离，又可以最小化类内样本的距离，而不会影响图像保真度和特征。