2015/09/03 by Mohamed Malek, David Helbert, Philippe Carre +1 · 7 citations
Computer Science · Engineering · #Advanced Graph Neural Networks #Advanced Image Fusion Techniques #Geodesic #Graph #Image Retrieval and Classification Techniques #Inpainting #Pattern recognition (psychology) #Wavelet #Wavelet transform #cs.CV
paper · pdf · doi:10.1117/1.jei.24.5.053004
published in Journal of Electronic Imaging 24(5), 053004 (SPIE)
openalex publication_date 2015/09/03 · arxiv created 2015/10/19 · arxiv updated 2015/10/28 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
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 of computing the graph of an image using the psychovisual information and analyzing 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 mean 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. Thus, results in image restoration highlight the interest of the appropriate use of color information.