Curvelet representation-based method for image underdetermined blind source separation

Abstract

The invention discloses a curvelet expression-based method for image underdetermined blind source separation. The method comprises the following steps: firstly, performing underdetermined blind sourceseparation under a sparse condition to acquire pre-separated signals in the same number as source signals; secondly, using the pre-separated signals as mixed signals to change an underdetermined state into an even-determined state; finally, adopting the conventional FastICA method to perform even-determined blind source separation to acquire final separated signals to realize the effect of the underdetermined image blind source separation. Aiming at the underdetermined blind source separation problem, the method provides a separation method based on underdetermined and even-determined combination, makes full use of a Curvelet domain coefficient property to complete initial underdetermined blind source separation, creates the mixed signals for conversion into even-determined blind source separation to adopt the FastICA to perform even-determined separation of the pre-separated signals at the same time, thereby improving the image blind source separation accuracy and improving image separation effect. The method has significant application potential in radio communication systems, sonar and radar systems, and audio, acoustic and medical signal processing in military and nonmilitaryfields.

Description

technical field [0001] The invention relates to an image noise reduction method, in particular to an image underdetermined blind separation method based on Curvelet representation. 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 separation methods, among which blind separation methods are the most representative. [0003] The blind separation method is to separate these mutually independent source signals only through the received mixed signal X when...

AI Technical Summary

Problems solved by technology

Wavelet transform is the main sparsification method. Although wavelet transform can efficiently analyze one-dimensional segmental continuous signals, for two-dimensional image processing, the two-dimensional wavelet transform formed by the tensor product of one-dimensional wavelet cannot express the "along "edge" (along edge) information, its sparsity has certain limitations

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