MDLatLRR: A novel decomposition method for infrared and visible image fusion
This work addresses image fusion for applications like surveillance or medical imaging, but it is incremental as it builds on existing decomposition techniques.
The authors tackled the problem of infrared and visible image fusion by proposing a multi-level decomposition method called MDLatLRR, which decomposes images into detail and base parts using latent low-rank representation, resulting in better fusion performance compared to state-of-the-art methods in subjective and objective evaluations.
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.