Context model and dual-tree complex wavelet transform-based denoising method for underwater sonar image

Abstract

The invention aims at providing a Context model and dual-tree complex wavelet transform-based denoising method for an underwater sonar image, comprising the step of: performing dual-tree complex wavelet decomposition on the underwater sonar image, wherein the low-frequency approximate component obtained by conducting the four-layer dual-tree complex wavelet decomposition on the image keeps invariable; performing denoising processing on the high-frequency component of the image; and performing dual-tree complex wavelet inverse transform on the processed complex wavelet coefficients so as to obtain the final image subjected to denoising. According to the denoising method, the similarity of energies among the dual-tree complex wavelet coefficients is measured by adopting the Context model, the coefficients with the similar energies are classified, different thresholds are determined aiming at each class of the coefficients, and denoising is realized by combining with a soft-threshold function. Threshold section is optimized; and more image details are reserved while the noise is removed, so that the phenomenon that the coefficients are excessively killed is suppressed.

Description

technical field [0001] The invention relates to a denoising method in the technical field of image processing. Background technique [0002] During the process of image acquisition and transmission, it is often disturbed by the external environment, resulting in image quality degradation, which seriously affects the subsequent image processing work. Therefore, image denoising preprocessing has also become a research hotspot in image processing, which provides a better research basis for subsequent high-level image processing. [0003] There are many types of image denoising methods, which are mainly divided into spatial domain methods and transform domain methods. The spatial domain method is to directly calculate the gray value of the image. The commonly used methods are: median filter, mean filter, non-local mean filter and so on. Both the median filter and the mean filter have a certain degree of edge blurring, and the non-local mean filter requires a large amount of ca...

AI Technical Summary

Problems solved by technology

The commonly used method in the transform domain method is the wavelet-based shrinkage threshold method. The traditional global threshold is not optimal for each transform coefficient, resulting in the loss of image detail information.

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