2024/05/10 by Charles-Gérard Lucas, Lucas, Charles-Gérard, Jérôme Gilles +1
Computer Science · #42C15 #42C40 #68U10 #FOS: Electrical engineering #Image and Signal Denoising Methods #Image and Video Processing (eess.IV) #Neural Networks and Applications #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2405.06188
openalex publication_date 2024/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The empirical wavelet transform is a data-driven time-scale representation consisting of an adaptive filter bank. Its robustness to data has made it the subject of intense developments and an increasing number of applications in the last decade. However, it has been mostly studied theoretically for signals and its extension to images is limited to a particular generating function. This work presents a general framework for multidimensional empirical wavelet transform based on any wavelet kernel. It also provides conditions to build wavelet frames for both continuous and discrete transforms. Moreover, numerical simulations of transforms are given.