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Ising granularity image analysis on VAE–GAN

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Abstract

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.

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Acknowledgements

The authors would like to acknowledge the financial support from National Key R &D Program of China (2019YFC0120102), Natural Science Foundation of Guangdong Province(No.2018A0303130169), Key scientific research platforms and projects of colleges and universities in Guangdong Province (No.2020ZDZX1023,No.2021ZDZX1062), National Natural Science Foundation of China (No.61772140), and the Opening Project of Guangdong Province Key Laboratory of Big Data Analysis and Processing at the Sun Yat-sen University(No.201902).

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Authors and Affiliations

  1. School of Computer Science, Guangdong University of Education, Guangzhou, 510303, Guangdong, China

    Guoming Chen, Qiang Chen & Yilin Wu

  2. Department of Computer Science, JiNan University, Guangzhou, 510632, Guangdong, China

    Shun Long, Zeduo Yuan & Weiheng Zhu

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  1. Guoming Chen
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  2. Shun Long
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  3. Zeduo Yuan
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  5. Qiang Chen
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Correspondence to Guoming Chen.

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Chen, G., Long, S., Yuan, Z. et al. Ising granularity image analysis on VAE–GAN. Machine Vision and Applications 33, 81 (2022). https://doi.org/10.1007/s00138-022-01338-2

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