MCADFusion: a novel multi-scale convolutional attention decomposition method for enhanced infrared and visible light image fusion
This paper presents a method called MCADFusion, a feature decomposition technique specifically designed for the fusion of infrared and visible images, incorporating target radiance and detailed texture. MCADFusion employs an innovative two-branch architecture that effectively extracts and decomposes...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-08-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2024233 |
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| _version_ | 1832590822259818496 |
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| author | Wangwei Zhang Menghao Dai Bin Zhou Changhai Wang |
| author_facet | Wangwei Zhang Menghao Dai Bin Zhou Changhai Wang |
| author_sort | Wangwei Zhang |
| collection | DOAJ |
| description | This paper presents a method called MCADFusion, a feature decomposition technique specifically designed for the fusion of infrared and visible images, incorporating target radiance and detailed texture. MCADFusion employs an innovative two-branch architecture that effectively extracts and decomposes both local and global features from different source images, thereby enhancing the processing of image feature information. The method begins with a multi-scale feature extraction module and a reconstructor module to obtain local and global feature information from rich source images. Subsequently, the local and global features of different source images are decomposed using the the channel attention module (CAM) and the spatial attention module (SAM). Feature fusion is then performed through a two-channel attention merging method. Finally, image reconstruction is achieved using the restormer module. During the training phase, MCADFusion employs a two-stage strategy to optimize the network parameters, resulting in high-quality fused images. Experimental results demonstrate that MCADFusion surpasses existing techniques in both subjective visual evaluation and objective assessment on publicly available TNO and MSRS datasets, underscoring its superiority. |
| format | Article |
| id | doaj-art-7979b402eea44899804da9040f20eeee |
| institution | Kabale University |
| issn | 2688-1594 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj-art-7979b402eea44899804da9040f20eeee2025-01-23T07:51:27ZengAIMS PressElectronic Research Archive2688-15942024-08-013285067508910.3934/era.2024233MCADFusion: a novel multi-scale convolutional attention decomposition method for enhanced infrared and visible light image fusionWangwei Zhang0Menghao Dai1Bin Zhou2Changhai Wang3Software Engineering College, Zhengzhou University of Light Industry, No.136 Science Road, Zhengzhou 450000, ChinaSoftware Engineering College, Zhengzhou University of Light Industry, No.136 Science Road, Zhengzhou 450000, ChinaElectronics and Electrical Engineering College, Zhengzhou University of Science and Technology, No.1 Xueyuan Road, Zhengzhou 450064, ChinaSoftware Engineering College, Zhengzhou University of Light Industry, No.136 Science Road, Zhengzhou 450000, ChinaThis paper presents a method called MCADFusion, a feature decomposition technique specifically designed for the fusion of infrared and visible images, incorporating target radiance and detailed texture. MCADFusion employs an innovative two-branch architecture that effectively extracts and decomposes both local and global features from different source images, thereby enhancing the processing of image feature information. The method begins with a multi-scale feature extraction module and a reconstructor module to obtain local and global feature information from rich source images. Subsequently, the local and global features of different source images are decomposed using the the channel attention module (CAM) and the spatial attention module (SAM). Feature fusion is then performed through a two-channel attention merging method. Finally, image reconstruction is achieved using the restormer module. During the training phase, MCADFusion employs a two-stage strategy to optimize the network parameters, resulting in high-quality fused images. Experimental results demonstrate that MCADFusion surpasses existing techniques in both subjective visual evaluation and objective assessment on publicly available TNO and MSRS datasets, underscoring its superiority.https://www.aimspress.com/article/doi/10.3934/era.2024233image fusionmulti-scaleconvolutional attention decompositionmodal specificityshared features |
| spellingShingle | Wangwei Zhang Menghao Dai Bin Zhou Changhai Wang MCADFusion: a novel multi-scale convolutional attention decomposition method for enhanced infrared and visible light image fusion Electronic Research Archive image fusion multi-scale convolutional attention decomposition modal specificity shared features |
| title | MCADFusion: a novel multi-scale convolutional attention decomposition method for enhanced infrared and visible light image fusion |
| title_full | MCADFusion: a novel multi-scale convolutional attention decomposition method for enhanced infrared and visible light image fusion |
| title_fullStr | MCADFusion: a novel multi-scale convolutional attention decomposition method for enhanced infrared and visible light image fusion |
| title_full_unstemmed | MCADFusion: a novel multi-scale convolutional attention decomposition method for enhanced infrared and visible light image fusion |
| title_short | MCADFusion: a novel multi-scale convolutional attention decomposition method for enhanced infrared and visible light image fusion |
| title_sort | mcadfusion a novel multi scale convolutional attention decomposition method for enhanced infrared and visible light image fusion |
| topic | image fusion multi-scale convolutional attention decomposition modal specificity shared features |
| url | https://www.aimspress.com/article/doi/10.3934/era.2024233 |
| work_keys_str_mv | AT wangweizhang mcadfusionanovelmultiscaleconvolutionalattentiondecompositionmethodforenhancedinfraredandvisiblelightimagefusion AT menghaodai mcadfusionanovelmultiscaleconvolutionalattentiondecompositionmethodforenhancedinfraredandvisiblelightimagefusion AT binzhou mcadfusionanovelmultiscaleconvolutionalattentiondecompositionmethodforenhancedinfraredandvisiblelightimagefusion AT changhaiwang mcadfusionanovelmultiscaleconvolutionalattentiondecompositionmethodforenhancedinfraredandvisiblelightimagefusion |