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DCNet: Differential computing-driven network for infrared and visible image fusion
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DOI：10.1016/j.imavis.2026.106086.png)
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Abstract
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• Proposes DCNet to fuse infrared and visible images with reduced noise. • Introduces DAB for cross-modal dependency modeling and noise suppression. • Designs DFEB to enhance differential features in channel and spatial domains. • Develops a gradient loss using Sobel and Laplacian operators.Develops a gradient loss function combining Sobel and Laplacian operators to constrain fusion quality with input image pairs.
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