A Degradation-Resistant Unfolding Network (DeRUN) for HIF (Heterogeneous image fusion) is a concept that unifies the interpretability of model-based methods and the generalizability of deep learning-based solutions. Extensive experiments have shown that DeRUN significantly outperforms existing methods on four HIF tasks as well as downstream applications using cheaper computational and memory costs.
Heterogeneous image fusion (HIF) aims to enhance image quality by merging complementary information of images captured by different sensors. Early model-based approaches have strong interpretability while being limited by non-adaptive feature extractors with poor generalizability.
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