Spatial-Spectral Transformer for Hyperspectral Image Denoising

2022/11/25 by Miaoyu Li, Ying Fu, Li, Miaoyu +3 · 8 citations
Computer Science · Engineering · #Advanced Image Fusion Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Signal Denoising Methods #Image and Video Processing (eess.IV) #Remote-Sensing Image Classification #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2211.14090

openalex publication_date 2022/11/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the subsequent HSI applications. Unfortunately, though witnessing the development of deep learning in HSI denoising area, existing convolution-based methods face the trade-off between computational efficiency and capability to model non-local characteristics of HSI. In this paper, we propose a Spatial-Spectral Transformer (SST) to alleviate this problem. To fully explore intrinsic similarity characteristics in both spatial dimension and spectral dimension, we conduct non-local spatial self-attention and global spectral self-attention with Transformer architecture. The window-based spatial self-attention focuses on the spatial similarity beyond the neighboring region. While, spectral self-attention exploits the long-range dependencies between highly correlative bands. Experimental results show that our proposed method outperforms the state-of-the-art HSI denoising methods in quantitative quality and visual results.

Cited by

• Next-Scale Prediction: A Self-Supervised Approach for Real-World Image Denoising
• Degradation-Aware Metric Prompting for Hyperspectral Image Restoration
• Iterative Low-rank Network for Hyperspectral Image Denoising
• Deep Equilibrium Convolutional Sparse Coding for Hyperspectral Image Denoising
• Physical Degradation Model-Guided Interferometric Hyperspectral Reconstruction with Unfolding Transformer
• A TRPCA-Inspired Deep Unfolding Network for Hyperspectral Image Denoising via Thresholded t-SVD and Top-K Sparse Transformer
• Only Large Weights (And Not Skip Connections) Can Prevent the Perils of Rank Collapse
• Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity

Related

• Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising
• Hybrid Spectral Denoising Transformer with Guided Attention
• Hybrid Spatial-spectral Neural Network for Hyperspectral Image Denoising
• Hybrid-Domain Synergistic Transformer for Hyperspectral Image Denoising
• Cross-Scope Spatial-Spectral Information Aggregation for Hyperspectral Image Super-Resolution