Distortion-Aware Routing and Parameter-Shared MoE for Multispectral Remote Sensing Super-Resolution

S 4×, and 45.94 dB on OLI2MSI 3×. Compared to the strongest baseline for each task, our method yields PSNR improvements of 0.12 dB, 0.10 dB, and 0.09 dB, respectively, while simultaneously reducing FLOPs and parameter counts. These results confirm that explicit distortion modeling and parameter-shared expert specialization provide an effective and computationally efficient solution for multispectral remote sensing image super-resolution.

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Publication Details

Journal
Sensors
Published
2026-04-01
DOI
https://doi.org/10.3390/s26072186
Primary Topic
Advanced Image Processing Techniques
Type
article
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article

Distortion-Aware Routing and Parameter-Shared MoE for Multispectral Remote Sensing Super-Resolution

Shuo Yang, Tianhui Zhang
Sensors
Advanced Image Processing Techniques
article

Distortion-Aware Routing and Parameter-Shared MoE for Multispectral Remote Sensing Super-Resolution

Shuo Yang, Tianhui Zhang
article en

Abstract

S 4×, and 45.94 dB on OLI2MSI 3×. Compared to the strongest baseline for each task, our method yields PSNR improvements of 0.12 dB, 0.10 dB, and 0.09 dB, respectively, while simultaneously reducing FLOPs and parameter counts. These results confirm that explicit distortion modeling and parameter-shared expert specialization provide an effective and computationally efficient solution for multispectral remote sensing image super-resolution.

SensorsVol. 26(7)
Chinese Academy of Sciences (CN), National Space Science Center (CN), University of Chinese Academy of Sciences (CN)
Sustainable cities and communities
Openalex Percentile: Top 37%
Advanced Image Processing Techniques
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