# SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising

arXiv: https://arxiv.org/abs/2012.01829 · HTML: https://mlsanity.com/paper/2012.01829/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2020.

## Built on by (most cited first, 12 of 12)

- [Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising](https://mlsanity.com/paper/2304.00844/) (2023)
- [A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration](https://mlsanity.com/paper/2111.09708/) (2021)
- [Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation](https://mlsanity.com/paper/2211.01825/) (2022)
- [Hyperspectral Image Denoising via Spatial-Spectral Recurrent Transformer](https://mlsanity.com/paper/2401.03885/) (2023)
- [Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising](https://mlsanity.com/paper/2305.04047/) (2023)
- [STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image Denoising](https://mlsanity.com/paper/2505.24327/) (2025)
- [Iterative Low-rank Network for Hyperspectral Image Denoising](https://mlsanity.com/paper/2509.00356/) (2025)
- [A TRPCA-Inspired Deep Unfolding Network for Hyperspectral Image Denoising via Thresholded t-SVD and Top-K Sparse Transformer](https://mlsanity.com/paper/2506.02364/) (2025)
- [Hybrid-Domain Synergistic Transformer for Hyperspectral Image Denoising](https://mlsanity.com/paper/2507.20099/) (2025)
- [Deep Equilibrium Convolutional Sparse Coding for Hyperspectral Image Denoising](https://mlsanity.com/paper/2508.15553/) (2025)
- [A Kernel Space-based Multidimensional Sparse Model for Dynamic PET Image Denoising](https://mlsanity.com/paper/2509.18801/) (2025)
- [Spatial-Spectral Adaptive Fidelity and Noise Prior Reduction Guided Hyperspectral Image Denoising](https://mlsanity.com/paper/2604.12600/) (2026)

## Abstract (arXiv)

Deep learning (DL) based hyperspectral images (HSIs) denoising approaches directly learn the nonlinear mapping between observed noisy images and underlying clean images. They normally do not consider the physical characteristics of HSIs, therefore making them lack of interpretability that is key to understand their denoising mechanism.. In order to tackle this problem, we introduce a novel model guided interpretable network for HSI denoising. Specifically, fully considering the spatial redundancy, spectral low-rankness and spectral-spatial properties of HSIs, we first establish a subspace based multi-dimensional sparse model. This model first projects the observed HSIs into a low-dimensional orthogonal subspace, and then represents the projected image with a multidimensional dictionary. After that, the model is unfolded into an end-to-end network named SMDS-Net whose fundamental modules are seamlessly connected with the denoising procedure and optimization of the model. This makes SMDS-Net convey clear physical meanings, i.e., learning the low-rankness and sparsity of HSIs. Finally, all key variables including dictionaries and thresholding parameters are obtained by the end-to-end training. Extensive experiments and comprehensive analysis confirm the denoising ability and interpretability of our method against the state-of-the-art HSI denoising methods.