Color graph based wavelet transform with perceptual information

TL;DR

This paper introduces a novel multiscale analysis method for color images using a graph-based wavelet transform that incorporates perceptual color information, improving image restoration tasks like denoising and inpainting.

Contribution

It presents a new graph construction method using psychovisual color distances and spectral graph wavelet analysis for enhanced color image processing.

Findings

Effective in denoising and inpainting tasks

Improves image restoration quality with perceptual color info

Promising results in preserving color fidelity

Abstract

In this paper, we propose a numerical strategy to define a multiscale analysis for color and multicomponent images based on the representation of data on a graph. Our approach consists in computing the graph of an image using the psychovisual information and analysing it by using the spectral graph wavelet transform. We suggest introducing color dimension into the computation of the weights of the graph and using the geodesic distance as a means of distance measurement. We thus have defined a wavelet transform based on a graph with perceptual information by using the CIELab color distance. This new representation is illustrated with denoising and inpainting applications. Overall, by introducing psychovisual information in the graph computation for the graph wavelet transform we obtain very promising results. Therefore results in image restoration highlight the interest of the appropriate use of color information.