Image processing method and device based on interpolation shear wave

Abstract

The invention relates to an image processing method and device based on interpolation shear wave, wherein the method comprises: employing a parameterized wavelet basis function to construct interpolation shear wave; according to the constructed interpolation shear wave, introducing a shear transformation matrix to construct an interpolation Shearlet function; according to the shear transformation matrix, performing shear transformation on an image to be processed; according to the interpolation Shearlet function, performing multi-scale interpolation wavelet transformation on the image to be processed after shear transformation so as to decompose and reconstruct the image to be processed; and through a threshold method, denosing the image to be processed after decomposition and reconstruction. The image processing method and device realize image multi-scale multi-direction decomposition, have a faster transformation speed, and can extend an image processing range to a Banach space from a Hilbert space, better maintain image textures, and more accurately identify image textures and noise, thereby avoiding artificial artifacts caused by texture diffusion.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an image processing method and device based on interpolation shear wave. Background technique [0002] Texture is an important feature for identifying objects in images. Therefore, the current image processing methods, especially the noise reduction process, hope not to destroy the texture structure of the image. The common method of image noise reduction is to regard the noise as a high-frequency signal, and perform noise reduction through various filters, such as mean filter, median filter, Wiener filter and wavelet transform. [0003] In fact, the texture, boundary contour, etc. in the image contain rich high-frequency information. The simple filtering method will inevitably destroy the texture and contour of the image while reducing the noise, so that the image after noise reduction is blurred and affects the subsequent processing of the image. . To solve ...

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Problems solved by technology

[0005] In view of the defect that the existing noise reduction method cannot correctly identify texture and noise, resulting in an unsatisfactory noise reduction effect, the present invention proposes the following technical solutions:

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