Using the Properties of Wavelet Coefficients of Time Series for Image Analysis and Processing

Abstract

Image processing is used in many fields of knowledge; because it allows to automate processes to get more information about the object being studied. Image processing techniques are many and varied. Wavelet analysis is one of such techniques. Among various methods and approaches of wavelet processing we distinguish the ideology of multiresolution wavelet analysis. The essence of this ideology is to perform wavelet decomposition on test data and the subsequent analysis of the relevant factors of this decomposition (the wavelet coefficients). An important aspect is the consideration of the properties of the wavelet coefficients. Based on this, we have examined the feasibility of using the properties of detailing wavelet coefficients to study and compare different images. We have introduced additional characteristics of images on the basis of sets of detailing wavelet coefficients decomposition. These characteristics reflect the dynamics of change in mean and variance for the detailing of the coefficients of the wavelet decomposition. We have shown that the dynamic changes in mean and variance of detailing coefficients of wavelet decomposition can be used to analyze and compare different images.