Nonlocal mean denoising method based on joint similarity

Abstract

The invention discloses a non-local mean denoising method based on joint similarity, belonging to the technical field of image processing. The method mainly solves the problem of inaccurate calculation of weight in the existing natural image non-local mean denoising technology. The implementation process of the method comprises the following steps of: (1) setting a searching region for all the pixels in an input noise-containing natural image, averaging and pre-selecting variance for the pixels in the searching region to obtain a similarity set; (2) calculating distance from the current pixelto all the pixels in the similarity set, and calculating the weight through a weight formula designed in the method; (3) performing weight average on all the pixels and corresponding pixel blocks in the similarity set according to the calculated weights of all the pixels in the similarity set to obtain gray values of image pixels and pixel blocks after pixel modification. The method provided by the invention is superior to other denoising methods in overall performance, can keep details of edges, textures and the like of the natural image while smoothing the noise better, and can be used for denoising treatment for the natural image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a non-local mean value denoising method based on joint similarity, which can be used for denoising processing of natural images. Background technique [0002] Image information has become an important source of information for human beings and an important means of using information due to its advantages of large amount of information, fast transmission speed, and long distance. However, images in reality are noisy due to various reasons. Noise deteriorates the image quality, makes the image blurred or even submerges and changes features, which brings difficulties to image analysis and recognition. In order to remove noise, it will cause blurring of image edges and loss of some texture details. Conversely, edge enhancement of an image will also enhance noise at the same time. Therefore, while removing noise, it is required to minimize the information of the image and kee...

AI Technical Summary

Problems solved by technology

Due to the good performance of this method in the field of denoising, it has attracted widespread attention from many scholars since it was proposed, but it still has the following problems: 1: The complexity of the algorithm is relatively large; 2: The accuracy of weight calculation is not good; 3: The edges and details of the image are still somewhat blurred

The corresponding weight function in the NL method is an exponential form, which means that the smaller the Euclidean distance between the corresponding blocks of two pixels, the greater the weight between the two pixels, which is considered in the actual physical sense. However, this exponential weight function has the disadvantages that the parameters are difficult to adapt and the weight distribution between similar points is unstable; in the BNL method, it is believed that the Euclidean distance between the corresponding blocks of two similar points obeys the chi-square distribution after correction, And transform this chi-square distribution into a Gaussian distribution, and design a weight function based on probability distribution, but this weight function is also very small when the distance between two similar points is small, which is practical physically wrong

[0006] To sum up, whether it is the NL method or the BNL method, their weight functions are flawed, which leads to inaccurate calculation of the similarity between pixels, and makes the restored value of the image pixel deviate too much from its true value.

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