Hyper-Restormer: A General Hyperspectral Image Restoration Transformer for Remote Sensing Imaging
Yo-Yu Lai, Chia-Hsiang Lin, and Zi-Chao Leng

TL;DR
Hyper-Restormer is a lightweight Transformer architecture designed for remote sensing hyperspectral image restoration, effectively capturing long-range dependencies while reducing computational complexity, and outperforming existing methods in denoising, inpainting, and super-resolution tasks.
Contribution
We introduce a novel lightweight spectral-spatial Transformer with low-rank decomposition for efficient remote sensing hyperspectral image restoration.
Findings
Outperforms state-of-the-art methods in denoising, inpainting, and super-resolution
Effectively captures long-range dependencies with reduced computational cost
Demonstrates superior restoration quality across various RS HSI tasks
Abstract
The deep learning model Transformer has achieved remarkable success in the hyperspectral image (HSI) restoration tasks by leveraging Spectral and Spatial Self-Attention (SA) mechanisms. However, applying these designs to remote sensing (RS) HSI restoration tasks, which involve far more spectrums than typical HSI (e.g., ICVL dataset with 31 bands), presents challenges due to the enormous computational complexity of using Spectral and Spatial SA mechanisms. To address this problem, we proposed Hyper-Restormer, a lightweight and effective Transformer-based architecture for RS HSI restoration. First, we introduce a novel Lightweight Spectral-Spatial (LSS) Transformer Block that utilizes both Spectral and Spatial SA to capture long-range dependencies of input features map. Additionally, we employ a novel Lightweight Locally-enhanced Feed-Forward Network (LLFF) to further enhance local…
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Taxonomy
TopicsAdvanced Image Fusion Techniques · Remote-Sensing Image Classification · Photoacoustic and Ultrasonic Imaging
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Residual Connection · Dropout · Layer Normalization · Dense Connections · Position-Wise Feed-Forward Layer · Absolute Position Encodings · Label Smoothing
