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

TL;DR

SMDS-Net is an interpretable deep learning model for hyperspectral image denoising that incorporates physical spectral-spatial properties, achieving effective denoising while maintaining interpretability through a model-guided network.

Contribution

The paper introduces a novel model-guided network that embeds physical spectral-spatial characteristics into deep learning for hyperspectral image denoising, enhancing interpretability and performance.

Findings

Outperforms state-of-the-art denoising methods in experiments.

Provides interpretable results aligned with physical spectral-spatial properties.

Demonstrates effective learning of low-rankness and sparsity in HSIs.

Abstract

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.