Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4101
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dc.contributor.authorBanerjee, Ankan-
dc.contributor.authorPatra, Dipti-
dc.contributor.authorRoy, Pradipta-
dc.date.accessioned2023-11-17T11:20:33Z-
dc.date.available2023-11-17T11:20:33Z-
dc.date.issued2023-11-
dc.identifier.citation8th International Conference On Computer Vision and Image Processing (CVIP) 2023 At IIT Jammu During 3rd -5th November 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4101-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractThis 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 re ned 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 ndings, 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 qualityen_US
dc.subjectimage fusionen_US
dc.subjectattentionen_US
dc.subjecthuman perceptionen_US
dc.subjectConvolutional Block Attention Moduleen_US
dc.titleImproved Multi-Modal Image Fusion with Attention and Dense Networks: Visual and Quantitative Evaluationen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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