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Mamba-UNet for reference-based super-resolution reconstruction.

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Title: Mamba-UNet for reference-based super-resolution reconstruction.
Authors: Ding, Bo1 (AUTHOR) dingbo@hrbust.edu.cn, Wang, Zihui1 (AUTHOR) wangzihui226@126.com, Wang, Huiqiang2 (AUTHOR) wanghuiqiang0505@foxmail.com, He, Yongjun2 (AUTHOR) heyongjun@hit.edu.cn, Zhou, Jun3 (AUTHOR) jun.zhou@griffith.edu.au
Source: Applied Intelligence. Jun2026, Vol. 56 Issue 8, p1-15. 15p.
Abstract: Reference-based super-resolution (RefSR) technology holds significant potential for enhancing the spatial resolution of remote sensing images. However, existing methods still face notable limitations in the effectiveness of large-scale texture transfer and struggle to guarantee the fidelity of re-constructed content when local variations exist between low-resolution (LR) images and reference (Ref) images. To address these problems, we propose a novel Mamba and U-Net-based method for RefSR, named Mamba-UNet. First, the LR image is spectrally decomposed into high-frequency (HF) and low-frequency (LF) components. The HF component is then enhanced by a Mamba module with long-sequence modeling capability, which can adaptively transfer textures from any location in the Ref image, thereby effectively resolving the blurring problem in large-scale texture transfer. Second, an adaptive multi-source data fusion module is designed to dynamically evaluate the land-cover consistency between the LR and Ref images through a channel attention gating mechanism. Once a local mismatch is detected, this mechanism automatically reduces the weight of Ref features and strengthens the dominance of the LR image’s own low-frequency content, thus ensuring the fidelity of content reconstruction. Finally, the gated fused features are fed into an attention-augmented U-Net for high-quality image reconstruction. Experimental results demonstrate that compared to state-of-the-art RefSR methods, the proposed method exhibits superior reconstruction performance and stronger robustness in quantitative metrics and visual quality assessments on the SECOND and CNAM-CD datasets. The code is available at [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Reference-based super-resolution (RefSR) technology holds significant potential for enhancing the spatial resolution of remote sensing images. However, existing methods still face notable limitations in the effectiveness of large-scale texture transfer and struggle to guarantee the fidelity of re-constructed content when local variations exist between low-resolution (LR) images and reference (Ref) images. To address these problems, we propose a novel Mamba and U-Net-based method for RefSR, named Mamba-UNet. First, the LR image is spectrally decomposed into high-frequency (HF) and low-frequency (LF) components. The HF component is then enhanced by a Mamba module with long-sequence modeling capability, which can adaptively transfer textures from any location in the Ref image, thereby effectively resolving the blurring problem in large-scale texture transfer. Second, an adaptive multi-source data fusion module is designed to dynamically evaluate the land-cover consistency between the LR and Ref images through a channel attention gating mechanism. Once a local mismatch is detected, this mechanism automatically reduces the weight of Ref features and strengthens the dominance of the LR image’s own low-frequency content, thus ensuring the fidelity of content reconstruction. Finally, the gated fused features are fed into an attention-augmented U-Net for high-quality image reconstruction. Experimental results demonstrate that compared to state-of-the-art RefSR methods, the proposed method exhibits superior reconstruction performance and stronger robustness in quantitative metrics and visual quality assessments on the SECOND and CNAM-CD datasets. The code is available at [ABSTRACT FROM AUTHOR]
ISSN:0924669X
DOI:10.1007/s10489-026-07292-0