Method for restraining image impulse noise based on iteration non-local mean value

Abstract

The invention discloses a method restraining image impulse noise based on an iteration non-local mean value. The method is mainly used for solving the problems that an existing method can not retain image detail information in the denoising process, and a sharp image can not be restored under high noise density. The method comprises the following realizing steps that (1) for a noise image, a histogram method is used for detecting the noise position; (2) an option switch median filter is used for carrying out pre-filtering on the noise image; (3) the iteration non-local mean value method is used for the image processed in a pre-filtering mode is carried out so that the sharp image with the impulse noise effectively restrained can be obtained. A simulation experiment shows that under the circumstance of the impulse noise of different degrees, the method is better in subjective visual effect and objective evaluation result compared with an existing algorithm, and the method can be used for restraining the high-density impulse noise and restoring the sharp image.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and in particular relates to a method for suppressing image pulse noise, which can be used to restore clear images from noise images polluted by high-density pulse noise. Background technique [0002] Impulse noise is composed of discontinuous, irregular pulses or noise spikes of short duration and large amplitude. Impulse noise can be generated by many factors, such as electromagnetic interference, faulty defects in communication systems, state changes of electrical switches and relays in communication systems, etc. In digital image processing, such as in the process of acquisition and transmission, the image cannot avoid the interference of impulse noise. Impulse noise causes random distribution of black and white noise points in the image, which greatly reduces the image quality and leads to subsequent transmission and A serious error occurred during processing. For example,...

AI Technical Summary

Problems solved by technology

[0004] First, these two methods regard the edge and detail pixels of the image as noise points in the filtering process, resulting in excessive smoothing and loss of image detail information.

[0005] Second, when the noise density increases, the accuracy and precision of the image restored by these two methods will drop sharply, which cannot meet the visual requirements of the human eye and the processing requirements of the computer.

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