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Hyperspectral image denoising method based on multichannel truncated nuclear norm and total variation regularization

A technology for truncating kernel norm and hyperspectral images, which is applied in the field of hyperspectral image denoising, which can solve the problems of complex hyperspectral image denoising, artifacts, uneven noise variance, etc., so as to maintain edge information and enhance The effect of the denoising effect

Pending Publication Date: 2020-12-15
ZHEJIANG UNIV OF TECH
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Problems solved by technology

Due to the characteristics of the sensor, the noise variance in different spectral bands will be unequal during the acquisition process, which makes the problem of hyperspectral image denoising more complicated. If the same processing is performed on each channel in the joint denoising process, there will be false positives. film

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  • Hyperspectral image denoising method based on multichannel truncated nuclear norm and total variation regularization
  • Hyperspectral image denoising method based on multichannel truncated nuclear norm and total variation regularization
  • Hyperspectral image denoising method based on multichannel truncated nuclear norm and total variation regularization

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Embodiment Construction

[0017] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0018] The multi-channel truncated kernel norm and total variation regularization hyperspectral image denoising model of the present invention comprises the following steps:

[0019] Step 1) Obtain the hyperspectral data image Y=X+N with to-be-denoised, where Y, X, N is additive white Gaussian noise (AWGN), X is the recovered clean image, where m and n are the length and width of the hyperspectral image space dimension, and p is the number of bands;

[0020] Step 2) construct the hyperspectral image denoising model of multi-channel truncated kernel norm and total variation regularization;

[0021] Further, step 2) the definition of multi-channel truncated nuclear norm total variation regularization model:

[0022]

[0023] where W is the weight matrix, is the Frobenius norm, β, λ are the balance parameters of the regularization term, ...

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Abstract

A hyperspectral image denoising method based on multichannel truncated nuclear norm and total variation regularization comprises the following steps: 1) obtaining a hyperspectral data image to be denoised, N being additive white Gaussian noise (AWGN), X being a recovered clean image, m and n being the length and width of the spatial dimension of the hyperspectral image respectively, and p being the number of spectral bands; 2) constructing a multi-channel truncated nuclear norm and total variation regularization hyperspectral image denoising model; (3) optimizing the model by adopting Alternating Direction Method of Multipliers (ADMM); and 4) outputting the denoised hyperspectral image. The method has the advantages that the piecewise smooth prior is better reserved, the edge information is effectively kept, and the denoising effect of Gaussian noise is enhanced at the same time.

Description

technical field [0001] The invention relates to the field of remote sensing image processing, in particular to a hyperspectral image denoising method. [0002] technical background [0003] Because hyperspectral remote sensing images contain rich spatial information and spectral information, hyperspectral images have attracted widespread attention in various application fields, such as urban planning, surveying and mapping, agriculture, forestry, and monitoring. However, the hyperspectral image (HSI) acquired by multi-detectors is usually corrupted by different types of noise, which seriously reduces the quality of the image and limits the accuracy of subsequent tasks such as classification, identification, unmixing, etc. Therefore, hyperspectral image denoising has very important value and significance in current academic research. [0004] In recent years, hyperspectral image denoising has attracted the attention of many scholars at home and abroad. So far, many different...

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Application Information

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IPC IPC(8): G06K9/00G06K9/40G06T5/00
CPCG06V20/194G06V20/13G06V10/30G06V10/513G06T5/70
Inventor 郑建炜周鑫杰陈培俊黄娟娟陈婉君秦梦洁
Owner ZHEJIANG UNIV OF TECH
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