Dictionary Learning Algorithm Based On Sparse Signal And Its Application

The main target of this paper is to research of dictionary learning algorithm used in sparserepresentation.Along with the rapid development of compressive sensing, the sparse representation ofsignals has become a hot research field. The overcomplete dictionary performs good in repre-sentation of signals, and is easy to represent a complex signal in a sparse coding. The overcom-plete dictionary designed by learning algorithm can represent a certain kind of signal adaptivelyto get a more sparse representation. This sort of problems have a wide application and highresearch value.This article conduct a study on designing a learned dictionary algorithm. In the research,traditional dictionary learning algorithm wasted a great quantity of computational complexity inthe early sparse representation stage, and may not achieve optimal for the overcoding of repre-sentation stage.Aiming at these problems, firstly proposed Global Support Alternative Pursuit K-SVDalgorithm(GSAP-KSVD), and improved the algorithm to obtain Alternative Pursuit Guided K-SVD(APG-KSVD) algorithm. In the end, this article experiment the proposed algorithm inimage denoising, and verified the advantage of proposed algorithm.