Infrared and visible image fusion using Latent Low-Rank Representation

TL;DR

This paper introduces a novel infrared and visible image fusion method based on latent low-rank representation, effectively combining global and local image structures to improve fusion quality.

Contribution

The proposed method leverages LatLRR to decompose images into low-rank and salient parts, enhancing fusion performance over existing techniques.

Findings

Outperforms state-of-the-art fusion methods in subjective evaluation.

Achieves better preservation of contour and salient features.

Demonstrates robustness across different image datasets.

Abstract

Infrared and visible image fusion is an important problem in the field of image fusion which has been applied widely in many fields. To better preserve the useful information from source images, in this paper, we propose a novel image fusion method based on latent low-rank representation(LatLRR) which is simple and effective. Firstly, the source images are decomposed into low-rank parts(global structure) and salient parts(local structure) by LatLRR. Then, the low-rank parts are fused by weighted-average strategy to preserve more contour information. Then, the salient parts are simply fused by sum strategy which is a efficient operation in this fusion framework. Finally, the fused image is obtained by combining the fused low-rank part and the fused salient part. Compared with other fusion methods experimentally, the proposed method has better fusion performance than state-of-the-art fusion methods in both subjective and objective evaluation. The Code of our fusion method is available at https://github.com/hli1221/imagefusion\_Infrared\_visible\_latlrr