LE2Fusion: A novel local edge enhancement module for infrared and visible image fusion
This addresses the challenge of preserving edge details in image fusion for downstream tasks like computer vision, though it appears incremental as it builds on existing fusion methods.
The authors tackled the problem of infrared and visible image fusion under complex illumination conditions by proposing LE2Fusion, a network that enhances local edge information, and demonstrated it outperforms state-of-the-art methods on public datasets.
Infrared and visible image fusion task aims to generate a fused image which contains salient features and rich texture details from multi-source images. However, under complex illumination conditions, few algorithms pay attention to the edge information of local regions which is crucial for downstream tasks. To this end, we propose a fusion network based on the local edge enhancement, named LE2Fusion. Specifically, a local edge enhancement (LE2) module is proposed to improve the edge information under complex illumination conditions and preserve the essential features of image. For feature extraction, a multi-scale residual attention (MRA) module is applied to extract rich features. Then, with LE2, a set of enhancement weights are generated which are utilized in feature fusion strategy and used to guide the image reconstruction. To better preserve the local detail information and structure information, the pixel intensity loss function based on the local region is also presented. The experiments demonstrate that the proposed method exhibits better fusion performance than the state-of-the-art fusion methods on public datasets.