CDNet converts coupled dictionary learning's unique-common prior into a joint unfolding architecture with block-sparse interaction and a high-low frequency fidelity loss, delivering competitive fusion performance at lower compute cost across four image fusion tasks.
Coupled Feature Learning for Multimodal Medical Image Fusion
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Combined Dictionary Unfolding Network with Gradient-Adaptive Fidelity for Transferable Multi-Source Fusion
CDNet converts coupled dictionary learning's unique-common prior into a joint unfolding architecture with block-sparse interaction and a high-low frequency fidelity loss, delivering competitive fusion performance at lower compute cost across four image fusion tasks.