Color graph based wavelet transform with perceptual information
This work addresses image restoration for computer vision by introducing a novel method that integrates psychovisual color data, though it appears incremental as it builds on existing graph wavelet transforms.
The authors tackled the problem of multiscale analysis for color images by proposing a graph-based wavelet transform incorporating perceptual color information, achieving promising results in denoising and inpainting applications.
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.