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Image blind separation based on sparse change

A blind source separation and sparse transformation technology, applied in the field of image noise reduction, can solve the problems that restrict the performance of image blind source separation methods, and cannot effectively describe two-dimensional or high-Vitch heterogeneous information.

Inactive Publication Date: 2008-11-12
SHANGHAI UNIV
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  • Abstract
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Problems solved by technology

However, the two-dimensional separable wavelet transform formed by the one-dimensional wavelet through the tensor product can only effectively represent one-dimensional singular information, that is, point singular information, but cannot effectively describe two-dimensional or high-dimensional singular information in the image, such as line , outline and other important information, which restricts the performance of image blind source separation method based on wavelet transform

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  • Image blind separation based on sparse change
  • Image blind separation based on sparse change
  • Image blind separation based on sparse change

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Embodiment Construction

[0040] A preferred embodiment of the present invention is described as follows in conjunction with accompanying drawing:

[0041] This image blind source separation method based on sparse transformation, such as figure 1shown. First, use the contour wavelet transform Contourlet to perform multi-scale and multi-directional sparse decomposition of the received mixed image signal, and use the sparsity criterion in the contour wavelet transform Contourlet domain to select the sub-image group with the best sparsity; then use the traditional The fast fixed-point independent component analysis method blindly separates the selected sub-image group to obtain the separation matrix; finally, use this separation matrix to separate the received mixed image signal, extract each independent component in the mixed image, and achieve the image blind source purpose of separation.

[0042] The specific steps are:

[0043] ①Initialize settings. Set the Laplastian decomposition LP decompositio...

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Abstract

First, using Contourlet transform, the method carries out multidirectional multiscale sparse decomposition for received signal of mixed image; in Contourlet transformed domain, using discriminating criterion of sparsity to select group of sub image with best sparsity; then, using traditional quick analysis method of independent components of fixed point to carry out blind separation for selected group of sub image so as to obtain separation matrix; finally, using the separation matrix to carrying out separation for received signal of mixed image, the method picks up each independent components in mixed image so as to reach purpose of separating images from blind sources. Raising precision for separating images from blind sources, the invention is applicable to radio communication system, sonar, and radar system as well as audio, acoustics and medicinal signal processing.

Description

technical field [0001] The invention relates to an image noise reduction method, in particular to an image blind source separation method based on sparse transformation. It has important application potential in image processing in military or non-military fields. Background technique [0002] Usually, the image will be polluted by other signals during its acquisition or transmission process, and it is necessary to carry out separation processing for subsequent further processing. The purpose of image separation is to extract the individual signal components in the received signal as much as possible to improve the quality of the image. At present, image noise reduction methods are mainly divided into traditional filtering methods and blind source separation methods, among which blind source separation methods are the most representative. [0003] The blind source separation method is to separate these mutually independent source signals only through the received mixed sig...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/40
Inventor 刘盛鹏方勇
Owner SHANGHAI UNIV