# Physics-Guided Deep Unfolding for Blind Cross-Sensor Spectral Super-Resolution via Learning the Spectral Transformation Function

arXiv: https://arxiv.org/abs/2606.05759 · HTML: https://mlsanity.com/paper/2606.05759/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Builds on (most cited first, 11 of 11)

- [Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2111.07910/) (2021)
- [MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction](https://mlsanity.com/paper/2204.07908/) (2022)
- [EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2310.19288/) (2023)
- [AdaptiveWeighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images](https://mlsanity.com/paper/2005.09305/) (2020)
- [Learned Spectral Super-Resolution](https://mlsanity.com/paper/1703.09470/) (2017)
- [Hyperspectral recovery from RGB images using Gaussian Processes](https://mlsanity.com/paper/1801.04654/) (2018)
- [Spectral Response Function Guided Deep Optimization-driven Network for Spectral Super-resolution](https://mlsanity.com/paper/2011.09701/) (2020)
- [Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB](https://mlsanity.com/paper/1709.00265/) (2017)
- [Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion](https://mlsanity.com/paper/2108.06073/) (2021)
- [Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield Prediction](https://mlsanity.com/paper/2401.11844/) (2024)
- [Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey](https://mlsanity.com/paper/2505.23248/) (2025)

## Abstract (arXiv)

Hyperspectral imaging provides rich spectral information for quantitative remote sensing, yet hyperspectral sensors remain costly and thus unavailable in many UAV deployments. Spectral super-resolution (SSR) seeks to reconstruct hyperspectral images (HSIs) from multispectral images (MSIs). Most existing SSR methods assume a fixed and known spectral response function (SRF) and are therefore limited to single-sensor settings. In practical cross-sensor scenarios, the spectral degradation from HSI to MSI is unknown and varies with sensor characteristics and scene content, which renders HSI reconstruction ill-posed. This paper proposes a physics-guided deep unfolding network, termed PGU-Net, to address blind cross-sensor SSR by jointly estimating the HSI and a learnable spectral transformation function (STF). PGU-Net unrolls an alternating optimization procedure into an end-to-end trainable architecture with stages, where each stage sequentially updates the HSI and the STF. Both modules combine learnable proximal networks with differentiable closed-form solvers, enabling physical interpretability while retaining strong representation capacity. Experiments on benchmark datasets (CAVE and NTIRE 2022) with multiple SRFs demonstrate accurate recovery of the STF (degradation operator) and improved reconstruction performance over state-of-the-art SSR methods. Furthermore, evaluations on a real UAV cross-sensor dataset (Headwall Nano HSI and DJI P4 Multispectral MSI) verify the effectiveness and robustness of PGU-Net under truly blind conditions, and suggest that the estimated STF may exhibit land-cover-related differences.