Image noise detection and denoising method based on Hessian matrix

Abstract

The invention discloses an image noise detection and denoising method based on a Hessian matrix, and the method comprises the steps: detecting a noise point through different characteristics of the feature value of the Hessian matrix of the noise point, an edge point and a smooth region point for impulse noise, employing the median filtering idea in a 3*3 window which takes the detected point as the center, employing the central value of the window to replace the noise point, and carrying out no processing for other points; proposing the concept of a noise point detection rate for the algorithm evaluation. Because the smooth region point or the edge point can be taken as the noise point during the detection of the noise point, a third discrimination condition is set for further improving the detection accuracy and denoising effect. For an image with the large noise density, the method also can obtain a better effect through multi-iteration. The method is better in denoising effect, and keeps more image edge and detail information. Compared with the median filtering, the method is better in effect under the condition of large noise density.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image noise detection and denoising method based on a Hessian matrix. Background technique [0002] Images are the main source for people to obtain external information. In digital images, noise mainly comes from the process of image acquisition and transmission. Noise not only affects the subjective visual effect of images, but also the quality of other image processing operations depends on the previous noise processing. Therefore, image denoising is very important in image preprocessing and is an important link in image processing. [0003] The main factor that affects the image quality in the process of image encoding and transmission is impulse noise. The removal of impulse noise has been extensively researched and developed rapidly. Among them, the median filter is better, but the traditional median filter is the All pixels of the entire image are replaced by t...

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Problems solved by technology

[0003] The main factor that affects the image quality in the process of image encoding and transmission is impulse noise. The removal of impulse noise has been extensively researched and developed rapidly. Among them, the median filter is better, but the traditional median filter is the All pixels of the entire image are replaced by the center value of the neighborhood window. Although it has a good denoising effect, all pixels of the image are processed, so important details such as edges and thin lines of the image are blurred. ; At the same time, when the impulse noise density is very large, the noise effect of median filtering can not meet the required requirements; in order to solve these problems, various improved median filtering algorithms have been proposed in recent years, such as weighted median filtering algorithm, switch median filter algorithm, adaptive median filter algorithm, minmax algorithm, extreme value median filter algorithm, etc., but there are more or less defects. Based on the above reasons, a new noise detection and removal method needs to be proposed. Noise method to meet the needs of use

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