Ising granularity image analysis on VAE–GAN

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.