Impulse noise elimination method of self-adaption normal-inclined double cross window mean filtering

Abstract

The invention discloses an impulse noise elimination method of self-adaption normal-inclined double cross window mean filtering and mainly solves the problem of an existing method that the impulse noise elimination effect is poor. The method comprises the realization steps of: (1) utilizing a sub-block ordering difference maximization method and a voting method to obtain upper and lower boundaries of impulse noise, and utilizing the upper and lower boundaries to detect impulse noise points; (2) firstly utilizing 3*3 vertical-horizontal cross (normal cross) windows to carry out 3 times of recursion cutting mean filtering on a noise image to be processed, then utilizing diagonal cross (inclined cross) windows to carry out 3 times of recursion cutting mean filtering, replacing values of the impulse noise points with results of cutting mean filtering, if the noise points are processed, ending meaning filtering, if not, increasing the windows for continuing the similar double cross window recursion cutting mean filtering, and ending the mean filtering when the windows are increased to 7*7windows; and (3) if noise processing is not completed, repeating the step (2) and forming iterated filtering. The impulse noise elimination method has the advantages that the impulse noise point detection is accurate, the impulse noise elimination effect is good, and the denoising speed is high.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an impulse noise elimination method for adaptive positive-slant double cross-window mean value filtering that can be used for digital image processing in many fields such as aerospace, military affairs, medicine, and astronomy. Background technique [0002] (1) Impulse noise and its model [0003] With the continuous development of pattern recognition and computer vision technology, people's requirements for image quality are getting higher and higher. However, the images are inevitably interfered by many external factors during the acquisition and transmission process, resulting in poor image quality. Impulse noise is one of the most important types of noise. It is discontinuous and consists of irregular pulses or noise spikes with short duration and large amplitude. Impulse noise can be divided into two categories: limited-range random-valued noise and ar...

AI Technical Summary

Problems solved by technology

However, the detection of noise points has become a new problem, especially the detection of impulse noise points. For example, some literature regards the difference between the median gray value of all pixels in the window and the gray value of the center point greater than the threshold as noise. Points, such as PSM (progressivemedian), TSM (tristatemedian) methods, these methods have the problem that the optimal threshold is difficult to choose, because the optimal threshold changes with the change of noise probability density and image, it is difficult to determine, and the image details Structural protection is weak

Some other documents perform noise detection based on the relationship between the gray value of a certain point in the image and the maximum value and minimum value of the gray value of the pixels in its neighborhood, and some documents use the gray value of a point in the image and the pixel points in its neighborhood to detect noise. The mean value relationship of the gray value is used for noise detection. The disadvantage of these literature methods is that non-noise points are misjudged as noise points.

In recent years, some scholars have proposed some new noise detection methods. For example, Ng et al. proposed a boundary detection method (BDND method): firstly, a 21×21 window is used for each pixel in the image to obtain neighborhood values ​​and sorted And use the method of the maximum value of the adjacent difference to initially determine the noise boundary, then use the 3×3 window to accurately determine the boundary, and finally use the boundary to determine the noise point; but this method not only has high computational complexity, b

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