Method for eliminating image impulse noise based on differential image detection and filtration by multiple windows

Abstract

The invention discloses a method for detecting impulse noise by utilizing differential images in three directions and then eliminating the image impulse noise through filtration by multiple windows. The method comprises the followings steps: (1) detecting positions of the impulse noise; (2) carrying out multiple-window self-adaptive filtration; and (3) cyclically detecting and filtering for several times. In the invention, the positions of the impulse noise are detected by utilizing the main features of the impulse noise, the impulse noise points are processed by combining the multiple-windowself-adaptive filtration, and the filtration is cyclically detected for several times so as to eliminate the impulse noise as much as possible, and image detail information is reserved. As long as animpulse noise image is input, a good filtering effect can be obtained. The invention can be used in the aspects of image restoration, reconstruction, and the like and can quickly restore the image quality.

Description

technical field [0001] The invention relates to computer image processing technology, in particular to a method for detecting impulse noise by using multi-directional differential images and removing image impulse noise by multi-window filtering. Background technique [0002] Digital image processing technology is a new discipline developed in the 1960s and 1970s. With the rapid development of computer technology, digital image processing technology has developed rapidly and formed many branches. Image denoising is an important content of image processing. During the acquisition and transmission of digital images, sensors and transmission channels often generate noise. The existence of noise greatly reduces the image quality, making image post-processing and segmentation, feature extraction, and target recognition difficult. Therefore, image denoising has become a very important task. Impulse noise is one of many types of noise. In the denoising process, it is required to ...

AI Technical Summary

Problems solved by technology

From the perspective of current technology, effective denoising algorithms are all targeted, such as for impulse noise or Gaussian noise, etc., the results of algorithms that can remove multiple types of noise are often unsatisfactory

[0003] For impulse noise, median filtering is currently the most widely used filtering method, and its effect is generally better, but because it does not distinguish all pixels, it will destroy and lose image details that are relatively small compared to the size of the filtering window, such as edges , sharp corners, etc., and make the pixels not affected by noise also be processed

[0004] In view of the shortcomings of the Median Filter (MF) algorithm in removing impulse noise, some more effective improved algorithms have been proposed in recent years, such as the weighted median filter, the maximum and minimum median filter, and the switch median filter. And so on, but these algorithms have different effects on different levels of impulse noise, especially when the noise increases to a certain level, the filtering effect of the improved method is not ideal, and the details are filtered to a large extent while filtering out the noise. The surface is also smoothed, and it does not overcome the sensitivity of the filtering algorithm to the noise intensity, which has great limitations

In general, the better multi-window adaptive filter does not detect the position of the noise, but only processes the entire image, resulting in the smoothing of some positions that are not noise, and when the noise is large, it is easy to agglomerate, and the effect is not good. it is good

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