Image de-noising method and device

Abstract

The present invention provides an image de-noising method and device. The method comprises: establishing a linear shift variant filtering linear function model qi=akIi+bk, and A-inversed I belongs to wi, wherein qi is the brightness value of a pixel point in the image after denoising, and Ii is a brightness value of the pixel point in an original image; performing filtering process of the original image, and obtaining an image to be filtered; employing the principle of infinite approaching of the brightness value qi of the pixel point in the image after denoising and the pixel value pi of the pixel point in the image to be filtered, and calculating the expression of the coefficient ak and the coefficient bk; performing window mean value filtering processing and pixel point operation of the original image and the image to be filtered according to the expression of the coefficient ak and the coefficient bk, and obtaining the concrete values of the coefficient ak and the coefficient bk in a window wk; and substituting the concrete values of the coefficient ak and the coefficient bk in the window wk into the function model qi=akIi+bk, and obtaining a de-noising image. The image de-noising method and device use window mean value filtering and pixel point operation to collect the calculation of the window target value, the algorithm flow is simple and easy to realize, and the linear function model can maintain the image edge characteristics.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an image denoising method and device. Background technique [0002] Since the image acquisition system will be interfered by various random signals such as temperature and electromagnetic waves, there will sometimes be obvious noise in the collected image, and many features contained in the image will be covered by the noise, resulting in poor image visual effect and data quality. poor. Therefore, it is undoubtedly of great theoretical and practical value to study image processing technology, weaken the influence of random noise on the image, increase the contrast and clarity of the image, and ensure the quality of image information. [0003] At this stage, the method of smoothing and filtering is usually used to eliminate the influence of noise. A better smoothing method should meet two conditions: firstly, to eliminate noise, and secondly, not to blur the edg...

AI Technical Summary

Problems solved by technology

[0004] The existing image denoising methods such as mean filtering and median filtering are based on a simple local linear or nonlinear model assumption, that is, the pixel value of a certain point on the image is considered to have a linear or nonlinear relationship with its adjacent parts. To a certain extent, the influence of noise can be removed, but it will blur the edge contour and lines of the image, which will affect the visual effect and recognition effect of the image.

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