Image decomposition is crucial for many image processing tasks, as it allows to extract salient features from source images. A good image decomposition method could lead to a better performance, especially in image fusion tasks. We propose a multi-level image decomposition method based on latent low-rank representation(LatLRR), which is called MDLatLRR. This decomposition method is applicable to many image processing fields. In this paper, we focus on the image fusion task. We develop a novel image fusion framework based on MDLatLRR, which is used to decompose source images into detail parts(salient features) and base parts. A nuclear-norm based fusion strategy is used to fuse the detail parts, and the base parts are fused by an averaging strategy. Compared with other state-of-the-art fusion methods, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation.
@article{arxiv.1811.02291,
title = {MDLatLRR: A novel decomposition method for infrared and visible image fusion},
author = {Hui Li and Xiao-Jun Wu and Josef Kittler},
journal= {arXiv preprint arXiv:1811.02291},
year = {2022}
}
Comments
IEEE Trans. Image Processing 2020, 14 pages, 17 figures, 3 tables. arXiv admin note: text overlap with arXiv:1804.08992