Inpainted Image Reconstruction Using an Extended Hopfield Neural Network Based Machine Learning System.

Wieslaw Citko,Wieslaw Sienko

doi:10.3390/s22030813

Abstract

This paper considers the use of a machine learning system for the reconstruction and recognition of distorted or damaged patterns, in particular, images of faces partially covered with masks. The most up-to-date image reconstruction structures are based on constrained optimization algorithms and suitable regularizers. In contrast with the above-mentioned image processing methods, the machine learning system presented in this paper employs the superposition of system vectors setting up asymptotic centers of attraction. The structure of the system is implemented using Hopfield-type neural network-based biorthogonal transformations. The reconstruction property gives rise to a superposition processor and reversible computations. Moreover, this paper’s distorted image reconstruction sets up associative memories where images stored in memory are retrieved by distorted/inpainted key images.

Highlights

Machine learning, a sub-field of artificial intelligence, deals with algorithms that build mathematical models to automatically make decisions or predictions based on sample data called training sets
We propose a machine learning model that uses biorthogonal transformations based on spectral processing as alternative solutions to deep learning based on optimization procedures
The aim of this article was to illustrate the potential for using the machine learning system shown in Figure 1 to reconstruct and recognize distorted or damaged patterns, in particular, images of people wearing masks

Summary

Introduction

A sub-field of artificial intelligence, deals with algorithms that build mathematical models to automatically make decisions or predictions based on sample data called training sets. In [1], we presented an example where the object of the reconstruction was an incomplete, inpainted image of a subject named Lena. Such examples of reconstruction allow for the development of a system based on the above-mentioned model of machine learning that can recognize people wearing masks. It is worth noting that the above-mentioned model of machine learning represents an alternative to classical image reconstruction/restoration systems, which make use of such processing tools as inverse modelling, deconvolution, Wiener filters, and PCA (Principal Component Analysis) [2,3,4,5]

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Methods

Discussion

Conclusion