Method for extracting image characteristics by multivariate gray model-based bi-dimensional empirical mode decomposition

Abstract

The invention discloses a method for extracting image characteristics by multivariate gray model-based bi-dimensional empirical mode decomposition, which belongs to the field of image processing and aims to solve the problem that image intrinsic information cannot be extracted effectively by the conventional bi-dimensional empirical mode decomposition method due to the influence of an end point effect. The method comprises the following steps of: performing boundary extension on a given image by using a multivariate gray model; performing the conventional bi-dimensional empirical mode decomposition on the extended image to acquire an extended bi-dimensional intrinsic mode function and a residual error; and extracting the bi-dimensional intrinsic mode function and the residual error at the position corresponding to the original image to be used as the final decomposition result. By the method, the advantages that a gray theory is low in data requirement quantity, high in short-term forecast accuracy and high in calculation speed and does not have particular requirements on the original data distribution are fully exerted, and the method is easy to implement, popularize and apply.

Description

technical field [0001] The invention relates to a method for extracting image features by using two-dimensional empirical mode decomposition based on a multivariate gray model, belonging to the field of image processing. Background technique [0002] In recent years, nonlinear and non-stationary data analysis methods have received extensive attention in the field of signal processing. Empirical Mode Decomposition (EMD), proposed by Norton Huang et al. in 1998, has become another new method after Fourier transform, wavelet transform, Wigner-Ville transform and other methods due to its powerful signal processing ability. research hotspots. In 2003, Nunes et al. extended EMD to two-dimensional situations, and proposed a two-dimensional empirical mode decomposition (Bi-dimensional Empirical Mode Decomposition, BEMD), which decomposes a given image into a limited number of two-dimensional local frequencies from high to low. The sum of Bi-dimensional Intrinsic Mode Function (BIM...

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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the existing two-dimensional empirical mode decomposition (Bi-dimensional Empirical Mode Decomposition, BEMD) method is affected by the endpoint effect and cannot effectively extract the intrinsic information of the image, and provides a method based on a multivariate gray model Two-dimensional Empirical Mode Decomposition Method for Extracting Image Features

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