Mixed-noise-resisting blind image source separating method based on feedback mechanism

A blind image source separation and noise mixing technology, which is applied in image enhancement, image data processing, instruments, etc., can solve problems such as unsatisfactory separation results, reduced separation effects, and reduced sparsity

Inactive Publication Date: 2013-09-11
BEIJING NORMAL UNIVERSITY
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Problems solved by technology

[0004] 1) The ICA-based algorithm can generally remove additive noise well, but the source signals are required to be independent and non-Gaussian. For image mixing with noise, it is difficult to ensure that only one branch is Gaussian property, thus causing unsatisfactory separation results (refer to comparative document 2);
[0005] 2) The SCA-based linear clustering algorithm has a good separation effect under the condition of no noise intervention and the mixed source satisfies the sparse type, but once the noise is involved, the sparsity is reduced, resulting in a sharp decline in the separation effect, and finally the source image cannot be correctly separated (refer to comparative document 3)

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  • Mixed-noise-resisting blind image source separating method based on feedback mechanism
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  • Mixed-noise-resisting blind image source separating method based on feedback mechanism

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Embodiment Construction

[0019] 1) Thinning. Carry out one-level integer wavelet transform to m mixed images X of the same size, select the diagonal component coefficient matrix, and expand them by row as row vectors of a matrix respectively, and this matrix is ​​the matrix for sparsening the mixed images;

[0020] 2) Remove zero columns and unify the direction. For each column X of the sparse matrix j (j=1, 2,..., T), if Satisfy X ij = 0,

[0021] Delete the jth column of X; if X 1j j =-X j . Process to get a new mixed signal X';

[0022] 3) Linear clustering. For any 2 column vectors X' of X' i and X' j ,like Then X' i and X' j collinear, let X′ i ∈θ(k),X′ j ∈θ(k), according to this linear clustering of all column vectors to get {θ|θ(k), k=1, 2,..., T};

[0023] 4) Estimate the mixing matrix A. Take the top m classes with the most clustering elements in θ, calculate the arithmetic mean of each class, and get the cluster center of the corresponding class, the cluster center matrix ...

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Abstract

The invention relates to a mixed-noise-resisting blind image source separating method based on a feedback mechanism. The method comprises the steps of utilizing wavelet transform to enable mixed signals to be sparse, carrying out linear cluster on sparse wavelet coefficients to estimate a hybrid matrix of a system, carrying out primary separation according to mixed images, then respectively calculating mean values of separating branches, taking out the most value to be outputted, utilizing a 0-setting feedback method to remove signals of the branch out of original mixed signals, carrying out next blind separation on the residual mixed signals in the method, and repeatedly carrying out the process until only a noise branch is left, namely completely separating all mixed signals. The method can effectively and blindly separate image mixing where Gaussian white noise participates. Compared with classis FastICA, the method can achieve higher separation accuracy.

Description

Technical field: [0001] The invention belongs to the intersection field of digital image processing and blind signal processing, and is a blind image source separation method based on feedback mechanism and anti-mixing Gaussian white noise. Background technique: [0002] Effective denoising is one of the biggest problems encountered in the signal processing field. In the blind source separation (BSS, blind source separation) technology originated from the "cocktail party", most researchers only consider removing channel additive noise (refer to Compare with file 1). In fact, as the source branch signal, there are not only conventional signal branches, but also noise source branches, which participate in the mixing of the system together. The model is as follows figure 1 shown. In the existing blind image source separation algorithm, there is no effective solution to the situation where the noise source branch participates in the system mixing. In summary, it is of great s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 余先川徐金东胡丹
Owner BEIJING NORMAL UNIVERSITY
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