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Multiplex Chemical Imaging Based on Broadband Stimulated Raman Scattering Microscopy

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Published on: July 25, 2022

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

Shuo Yang^1,2, Shi Chen¹, Yuxuan Liu^1,2

¹National Space Science Centre, Chinese Academy of Sciences, Beijing 100190, China.

Sensors (Basel, Switzerland)

|April 14, 2026

Summary

This summary is machine-generated.

This study introduces a novel framework for multispectral remote sensing image super-resolution (RSISR) that effectively handles complex distortions. The proposed method enhances image detail and cross-band consistency efficiently, outperforming existing techniques.

Keywords:

low-rank adaptation mixture-of-experts multispectral imaging parameter-efficient fine-tuning remote sensing super-resolution

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Area of Science:

Remote Sensing
Computer Vision
Image Processing

Background:

Multispectral remote sensing image super-resolution (RSISR) faces challenges due to heterogeneous distortions in satellite imagery.
Uniform restoration strategies are suboptimal for complex degradations like band-dependent noise and spatially varying texture issues.

Purpose of the Study:

To develop a unified framework for RSISR that addresses heterogeneous distortions and computational constraints.
To improve high-frequency detail reconstruction and preserve cross-band structural consistency in degraded multispectral images.

Main Methods:

A Distortion-Aware Feature Extractor (DAFE) synthesizes distortion cues including frequency bases, residual components, edge representations, and noise proxies.
A Distortion-Aware Expert Choice (DAEC) router assigns experts based on distortion cues for capacity-constrained, load-balanced restoration.
A parameter-shared Mixture-of-Experts (PS-MoE) architecture with band-wise low-rank adapters enables efficient, coarse-to-fine restoration.

Main Results:

Achieved high PSNR values: 49.38 dB (SEN2VENμS 2×), 45.91 dB (SEN2VENμS 4×), and 45.94 dB (OLI2MSI 3×).
Outperformed strongest baselines with improvements of 0.12 dB, 0.10 dB, and 0.09 dB respectively.
Significantly reduced Floating Point Operations (FLOPs) and parameter counts, demonstrating computational efficiency.

Conclusions:

Explicit distortion modeling and parameter-shared expert specialization offer an effective solution for multispectral RSISR.
The proposed framework provides a computationally efficient approach to enhance remote sensing image quality.
The method successfully balances restoration performance with reduced computational overhead.