Non-local Similarity Complex Domain Denoising for Hyperspectral Phase Imaging

被引：0
|
作者：
Katkovnik, V [1 ]
Shevkunov, I [1 ]
Claus, D. [2 ]
Pedrini, G. [3 ]
Egiazarian, K. [1 ]
机构：
[1] Tampere Univ, Lab Signal Proc, FIN-33101 Tampere, Finland
[2] Inst Lasertechnol Med & Messtech, Helmholtzstr 12, D-89081 Ulm, Germany
[3] Univ Stuttgart, ITO, Pfaffenwaldring 9, D-70569 Stuttgart, Germany
来源：
OPTICS, PHOTONICS AND LASERS (OPAL 2019) | 2019年
基金：
芬兰科学院; 欧盟地平线“2020”;
关键词：
Hyperspectral imaging; Noise in imaging systems; Complex domain imaging; Complex domain sparsity;
D O I：
暂无
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
In this paper, we propose a novel complex domain denoising algorithm for hyperspectral data. The algorithm is based on a Complex Domain Block-Matching 3D (CDBM3D) filter and on similarity of hyperspectral data, which are usually slow varying for close values of wavelengths. The SVD of the hyperspectral data is used in order to define an optimal small dimension data subspace. The CDBM3D is applied in this subspace only. The efficiency of the algorithm is demonstrated in simulation and on experimental data obtained from spectrally resolved digital holography for a transparent color object. It is proved that the proposed filtering algorithm retrieves amplitude and phase distributions even from very noisy data.
引用
收藏
页码：11 / 15
页数：5
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