Year: 2017
Type: article
Abstract: The letter addresses the image fusion problem, where multiple images captured with different focus distances are to be combined into a higher quality all-in-focus image. Most current approaches for image fusion strongly rely on the unrealistic noise-free assumption used during the image acquisition, and then yield limited fusion robustness. In our approach, we formulate the multifocus image fusion problem in terms of an analysis sparse model, and simultaneously perform the restoration and fusion of multifocus images. Based on this model, we propose an analysis operator learning, and define a novel fusion function to generate an all-in-focus image. Experimental evaluations confirm the effectiveness of the proposed fusion approach both visually and quantitatively, and show that our approach outperforms the state-of-the-art fusion methods.
Source: IEEE Signal Processing Letters
Authors: Rui Gao, Sergiy A. Vorobyov, Hong Zhao
Institutions: Northeastern University, Aalto University
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: Academy of Finland
Award: G3179654713
OpenAlex ID: W2606716941