论文信息 - Hyperspectral phase imaging with denoising in SVD image subspace

Hyperspectral phase imaging with denoising in SVD image subspace

We propose a modified denoising algorithm for hyperspectral data. The algorithm is based on a complex domain block-matching 3D filter, on estimation of the noise correlation matrix and on dimension reduction of the Singular Value Decomposition (SVD) eigenspace.

Karen O. Egiazarian | Vladimir Katkovnik | Giancarlo Pedrini | Igor Shevkunov | Daniel Claus

[1] Hajime Matsumine,et al. Hyperspectral Imaging Provides Early Prediction of Random Axial Flap Necrosis in a Preclinical Model , 2017, Plastic and reconstructive surgery.

[2] G. Pedrini,et al. Accuracy enhanced and synthetic wavelength adjustable optical metrology via spectrally resolved digital holography. , 2018, Journal of the Optical Society of America. A, Optics, image science, and vision.

[3] M. Govender,et al. A review of hyperspectral remote sensing and its application in vegetation and water resource studies , 2009 .

[4] José M. Bioucas-Dias,et al. Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5] S. Kalenkov,et al. Hyperspectral holography: an alternative application of the Fourier transform spectrometer , 2017 .

[6] Maksim S. Kulya,et al. On terahertz pulsed broadband Gauss-Bessel beam free-space propagation , 2018, Scientific Reports.

[7] José M. Bioucas-Dias,et al. Hyperspectral Subspace Identification , 2008, IEEE Transactions on Geoscience and Remote Sensing.