Infrared and visible image fusion based on domain transform filtering and sparse representation

Infrared and visible image fusion is an important branch in the field of information fusion. Fused images can provide more comprehensive and objective interpretations of complex scenes with confusing or hidden information. This paper proposes a novel infrared and visible image fusion method based on domain transform filtering (DTF) and sparse representation (SR). First, infrared and visible images are decomposed using a low-pass filter into base and detail layers, respectively. An SR-based rule is designed to fuse the detail layers. To improve the feature expression ability of a redundant dictionary, we introduce a multiscale detail enhancement technique to preprocess the training data. Additionally, a fast but effective rule is proposed based on salient structure extraction and DTF base layer fusion. The structure and border information of source images can be successfully transformed into the fused image. The proposed method can effectively retain the salient structure and edge information of the source images in the fused image. Experimental results demonstrate that the proposed method can outperform state-of-the-art methods in terms of both subjective visual and objective quantitative evaluations. The source code of the proposed method is released at https://github.com/ixilai/DTF-SR.