Image noise filtering method via median and mean value iterative filtering of minimal cross window

Abstract

The invention discloses an image noise filtering method via median and mean value iterative filtering of a minimal cross window, and aims at solving the problems that a present image de-noising method is hard to protect edge details and set parameters during de-noising. The image noise filtering method is realized by the steps that (1) noise points are found via an extremum method, and a binary map whose size is the same with that of an image is constructed; (2) the noise density p is calculated; (3) if p < / = 0.525, the iterative median of the minimal cross window is used for filtering, namely, the median of non-noise points in the minimal cross window replaces the value of each noise point in the noise image, iteration is carried out for another two times, whether all the noise points are processed is determined after iteration in each time, and if yes, a de-noising result is output; and (4) if p>0.525, the iterative mean value of the minimal cross window is used for filtering in a way similar to that via the iterative median. The image noise filtering method has the advantages that it is not required to consider the size of windows, operation is easy, the de-noising efficiency is high, details of the image are kept effectively, and the method is more suitable for real-time application.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an image noise filtering method for minimum cross-window median and mean value iterative filtering that can be used in digital image processing in fields such as aerospace, industry, agriculture, medicine, and military affairs. Background technique [0002] In the process of scene imaging, spatial sampling and quantization, the image is often disturbed by various external noises, which degrades the image quality. Image noise is the most direct, harmful and critical problem affecting human observation. In order to reduce the influence of noise as much as possible, the noise-contaminated image must be denoised. In 1971, the famous scholar Tukey proposed a nonlinear filter-median filter in his pioneering paper. The research shows that the median filter can overcome the image blur caused by the linear filter under certain conditions, and it is the best way to e...

AI Technical Summary

Problems solved by technology

However, as pointed out in many literatures, the median filter is extended from one-dimensional signal to multi-dimensional signal processing. Although the median filter has a good ability to suppress narrow-pulse interference, points, thin lines, and sharp vertex angles in two-dimensional After the value is filtered, it may be lost; in addition, its denoising effect is related to the size of the window. The smaller the window, the worse the denoising effect, the better the protection of details, and the faster the running speed. On the contrary, the larger the window, the better the denoising effect, and the slower running speed. , poorer protection of details

Because of the defects of the standard median filter, various improved median filter algorithms have emerged, such as adaptive median filter algorithm, switch median filter algorithm, weighted median filter algorithm, three-state median filter algorithm, soft switching median filter algorithm, etc. Value filtering, etc. These algorithms have good improvement effects in denoising and protecting details, but the processing speed is not ideal and cannot meet real-time requirements.

For example, the traditional median filter algorithm sorts the pixels in each window to find the median value, and its complexity (measured by the number of comparisons) is proportional to the square of the number of sorted objects, and when the size of the window increases, the amount of calculation It will increase according to the fourth power, it is difficult to meet the real-time requirements of digital image processing

Based on this, Zhang Xinming and others proposed a fast adaptive image median filtering method based on cross sliding windows (Zhang Xinming, Dang Liuqun, Xu Jiucheng. Fast adaptive image median filtering based on cross sliding windows. Computer Engineering and Application, 2007, 43(27): 37-39.), which improves the detail protection ability and running speed, but this method is not used because the information utilization rate of non-noise points in the image is not high, and it uses non-clipping median filtering and other reasons, so it is not used limited noise effect

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