Improved Multi-modal Image Fusion with Attention and Dense Networks: Visual and Quantitative Evaluation

This article introduces a novel multi-modal image fusion approach based on Convolutional Block Attention Module and dense networks to enhance human perceptual quality and information content in the fused images. The proposed model preserves the edges of the infrared images and enhances the contrast of the visible image as a pre-processing part. Consequently, the use of Convolutional Block Attention Module has resulted in the extraction of more refined features from the source images. The visual results demonstrate that the fused images produced by the proposed method are visually superior to those generated by most standard fusion techniques. To substantiate the findings, quantitative analysis is conducted using various metrics. The proposed method exhibits the best Naturalness Image Quality Evaluator and Chen-Varshney metric values, which are human perception-based parameters. Moreover, the fused images exhibit the highest Standard Deviation value, signifying enhanced contrast. These results justify the proposed multi-modal image fusion technique outperforms standard methods both qualitatively and quantitatively, resulting in superior fused images with improved human perception quality.