Renormalization. Method for Examining Digital Image Processing Problems based on Renormalization Group Ideas

Abstract or Introduction

This paper presents a method for Examining Digital Image Processing Problems based on Renormalization Group Ideas, Markov Random Field Modeling of Images, and Metropolis-Type Monte Carlo algorithm, pro. The method can be used effectively in combination and can be used in rehabilitation, drug control tissue, coding, movement analysis, etc. It provides integration to perform hierarchical, multi-scale, coarseto-fine analysis of functional images such as The technique was developed and used for the restoration of distorted images. Inverse algorithms are global optimization algorithms used for other optimization problems. It iteratively creates multilevel cascades of recovered images at different resolution or scale levels.

Image processing is hard work, especially when it comes to images with complex patterns such as textures or fractals. Traditional image processing techniques may not be sufficient to extract features from such images. However, the Renormalization Group (RG) method provides a hierarchical and systematic way to analyze images at different scales and extract their associated features. The purpose of this document is to provide an overview of the conversion suite for image processing and its applications.