超光譜圖像去噪基準

時間 2019-11-09

標籤光譜圖像基準简体版

原文原文鏈接

根據是否聯合利用超光譜圖像的空間和光譜信息，高光譜圖像去噪技術能夠分爲兩類。第一類就是將傳統 2-D 圖像去噪的方法直接應用到超光譜圖像的每一個頻帶上去，稱爲逐帶去噪。第二類就是聯合利用空間和光譜信息來進行去噪，稱爲聯合去噪，這又能夠大體分爲基於變換域的方法和基於空間域的方法。除此以外，因爲深度理論的興起，最近也出現了一些基於深度學習的超光譜圖像去噪方法。php

逐帶去噪

[BM3D] Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering, TIP2007, K. Dabov et al.
[WNNM] Weighted nuclear norm minimization with application to image denoising, CVPR2014, S. Gu et al.
[EPLL] From learning models of natural image patches to whole image restoration, ICCV2011, D. Zoran et al.

然而，這些逐帶去噪方法一般致使更大的頻譜失真，由於沒有同時考慮不一樣頻帶之間的空間和頻譜信息的相關性。html

聯合去噪

1) 基於變換域的方法

基於變化域的方法嘗試經過不一樣的變換來將乾淨信號從噪聲數據中分離出來，好比主成分分析、傅里葉變換、小波變換。git

Wavelet-based hyperspectral image estimation, IGARSS2003, I. Atkinson et al.
Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage, TGRS2006, H. Othman et al.

這一類方法的主要缺點是它們對變換函數的選擇很敏感，而且沒有考慮超光譜圖像幾何特徵的差別。github

2) 基於空間域的方法

採用合理的假設或先驗，如譜間全局相關性(Global Correlation along Spetrum) 、空間非局部自類似性(Non-local Self Similarity across space)、總變差(Total Variation)、非局部(Non-Local)、稀疏表示(Sparse Representation)、低秩模型(Low Rank models)等，基於空間域的方法能夠將噪聲超光譜圖像映射到乾淨圖像而且保持其空間和光譜特徵。app

[GCS and NSS] Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Denoising, ICCV2015, Ying Fu et al.dom
[GCS and NSS] Decomposable nonlocal tensor dictionary learning for multispectral image denoising, CVPR2014, P. Yi et al.函數
[GCS and NSS] Multispectral images denoising by intrinsic tensor sparsity regularization, CVPR2016, Q. Xie et al.學習
[GCS] Denoising of hyperspectral images using the parafac model and statistical performance analysis, TGRS2012, X. F. Liu, et al.google
[TV] Hyperspectral image denoising employing a spectral–spatial adaptive total variation model, TGRS2012, Q. Yuan et al.spa
[TV] Hyperspectral image denoising with a combined spatial and spectral hyperspectral total variation model, CJRS2014, G. Chen et al.
[NL] A nonlocal transform-domain filter for volumetric data denoising and reconstruction, TIP2012, M. Maggioni et al.
[SR] Spectral–Spatial Adaptive Sparse Representation for Hyperspectral Image Denoising, TGRS2016, T. Lu et al.
[SR] Noise removal from hyperspectral image with joint spectral-spatial distributed sparse representation, TGRS2016, J. Li et al.
[LR] Denoising and dimensionality reduction using multilinear tools for hyperspectral images, GRSL2008, N. Renard et al.
[LR] Hyperspectral image restoration using low-rank matrix recovery, TGRS2014, H. Zhang et al.
[LR] Hyperspectral image denoising via sparse representation and low-rank constraint, TGRS2015, Y. Zhao et al.
[LR] Hyperspectral image restoration via iteratively regularized weighted Schatten p-norm minimization, TGRS2016, Y. Xie et al.
[LR] Hyperspectral image restoration using low-rank tensor recovery, J-STARS2017, H. Fan et al.

深度學習去噪

Hyperspectral imagery denoising by deep learning with trainable nonlinearity function, GRSL2017, W. Xie et al.
Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network, TGRS2018, Q. Yuan et al. [code]

數據集

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去噪效果評價指標

Peak Signal to Noise Ratio (PSNR)
Structural SIMilarity index (SSIM)
Feature SIMilarity index (FSIM)
Erreur Relative Globale Adimensionnelle de Synthèse (ERGAS)
Spectral Angle Mapper (SAM)

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