Image denoising method based on network function interpolation

Abstract

The invention provides an image denoising method based on net function interpolation, and the method comprises the following steps: S1, carrying out the processing of a noise-containing target image, and detecting the noise point of the target image; S2, constructing a denoising rectangle, wherein the denoising rectangle comprises a noise point; S3, establishing a rectangular coordinate system, placing the de-noised rectangle in the rectangular coordinate system, making two vertical lines on two adjacent sides of the de-noised square rectangle, enabling the two vertical lines to pass through noise points, and obtaining Pi of four vertexes of the de-noised rectangle and values of intersection points of the two vertical lines and the four sides of the rectangle; s4, constructing a network function interpolation model; carrying out T times of iteration by adopting a net function interpolation model; wherein the obtained difference function value F(Q) is the real image value of the denoised noise point Q. Through the method, noise contained in the image can be effectively removed, the denoising process is simple, the efficiency is high, and the denoising process can be highly consistent with the original image, so that the final denoised image effect is ensured.

Description

technical field [0001] The invention relates to an image processing method, in particular to an image denoising method based on network function interpolation. Background technique [0002] The image will be affected by uncontrollable factors (equipment, environment, etc.) during the imaging, transmission and storage process, which will cause image data distortion and disturb the observable information of the image. Therefore, there are various methods for image denoising. In some denoising methods, algorithms such as mean filter, adaptive Wiener filter, and termination filter are used for processing. However, in the existing methods, the existing processing algorithm is complicated, so the processing efficiency is low. Moreover, in the existing algorithms, it is easy to cause changes in the details of the image, resulting in poor effects of the final denoised image, which is obviously inconsistent with the original image. Contents of the invention [0003] In view of thi...

AI Technical Summary

Problems solved by technology

[0002] The image will be affected by uncontrollable factors (equipment, environment, etc.) during the imaging, transmission and storage process, which will cause image data distortion and disturb the observable information of the image. Therefore, there are various methods for image denoising. In some denoising methods, algorithms such as mean filter, adaptive Wiener filter, and termination filter are used for processing. However, in the existing methods, the existing processing algorithm is complicated, so the processing efficiency is low. Moreover, in the existing algorithms, it is often easy to cause changes in the details of the image, resulting in a poor image effect after the final denoising, which is obviously inconsistent with the original image.

Images