Blind image separation method based on frequency-domain sparse component analysis

Abstract

The invention relates to a blind image separation method based on frequency-domain sparse component analysis. In the currently-provided blind source separation algorithm, an independent component analysis method with better separation effect has a blind source separation premise that source signals are not in Gaussian distribution, are mutually independent, and can not thoroughly separate sub-Gaussian signals in image signals. The sparse component analysis is a novel blind source separation technology developed in recent years, by applying the technology, source signals are extracted by utilizing the sparse properties of the signals and the better separation effect is obtained. The image signals which do not satisfy sparse conditions can not be separated by applying a traditional sparse component analysis model. In the invention, the images are converted into the frequency domain from the space domain by combining the characteristic that the images are sparse in the frequency domain space and utilizing sparseness algorithms, such as wavelet transform, and the like; a sparse component analysis model is educed in the frequency domain; and a hybrid matrix estimation method and a source signal estimation method based on linear-clustering sparse component analysis are provided; therefore, the source images are extracted. Experiments prove that the method of the invention has the separation precision up to 100 percent and is superior to other separation methods.

Description

Technical field: [0001] The invention belongs to the technical field of blind source separation, and in particular relates to a blind image separation method based on frequency domain sparse component analysis. Background technique: [0002] Blind source separation is an active branch of signal processing. It refers to recovering or extracting the source signal by using only a set of acquired mixed signals (mixed from the source signal) without knowing the distribution of the source signal or the mixing model of the source signal. Blind source separation has been applied in many fields, such as biomedical image signal processing, speech signal processing, image restoration and understanding, etc. (Refer to Reference 1). [0003] Image blind separation is a kind of blind source separation, except that the signal involved in blind separation is image signal. The general blind source separation method can be used for image blind separation. [0004] Blind source separation a...

AI Technical Summary

Problems solved by technology

[0011] Aiming at the defect that the blind source separation method proposed so far has incomplete separation results and low separation precision, the present invention proposes a blind image separation method for sparse component analysis in the frequency domain

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