Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations

Abstract

This paper introduces two very fast and competitive hyperspectral image (HSI) restoration algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with Gaussian and Poissonian noise, and fast hyperspectral inpainting (FastHyIn), an inpainting algorithm to restore HSIs where some observations from known pixels in some known bands are missing. FastHyDe and FastHyIn fully exploit extremely compact and sparse HSI representations linked with their low-rank and self-similarity characteristics. In a series of experiments with simulated and real data, the newly introduced FastHyDe and FastHyIn compete with the state-of-the-art methods, with much lower computational complexity.

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• Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation
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• Hyperspectral Image Denoising via Self-Modulating Convolutional Neural Networks
• Connections between Deep Equilibrium and Sparse Representation Models with Application to Hyperspectral Image Denoising
• Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization
• Hyperspectral Image Denoising via Spatial-Spectral Recurrent Transformer
• A Comprehensive Comparison of Multi-Dimensional Image Denoising Methods
• HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Package
• Self-supervised Hyperspectral Image Restoration using Separable Image Prior
• FastHyMix: Fast and Parameter-free Hyperspectral Image Mixed Noise Removal
• Hyperspectral Mixed Noise Removal via Subspace Representation and Weighted Low-rank Tensor Regularization
• Spatial-Spectral Adaptive Fidelity and Noise Prior Reduction Guided Hyperspectral Image Denoising
• Multispectral CT Denoising via Simulation-Trained Deep Learning: Experimental Results at the ESRF BM18