Signal-dependent noise parameter estimation method based on improved density peak clustering

Abstract

The invention discloses a signal-dependent noise parameter estimation method based on improved density peak clustering. A sample is extracted from an image containing noise through a sliding window, and a mean value, entropy and gradient are calculated to serve as feature data. And inputting into a clustering algorithm for clustering, and distinguishing weak texture samples from strong texture samples. In the clustering process, the concept of relative density is introduced, comparison ranges are divided through the distance between data points and surrounding data, the relative density of the data points in each comparison range is calculated, and finally the data points with high relative density are selected as clustering centers. The problem that when a traditional DPC algorithm is used for clustering a data set with non-uniform density, a sparse cluster center is often neglected, so that the clustering precision is influenced is solved. And according to a clustering result, pixel level estimation and noise estimation are carried out on a sample whose cluster label is weak texture, and finally a pixel value-noise variance estimation pair is fitted through a least square method, so that a noise parameter estimation value of an original image is obtained, and the preparation work of denoising is realized.

Description

technical field [0001] The invention belongs to the technical field of image noise signal processing, and in particular relates to a signal-dependent noise parameter estimation method based on improved density peak clustering. Background technique [0002] Image is an important carrier of information recording and transmission in modern society, but the image will inevitably be affected by noise during the process of collection, storage and transmission, which will reduce the quality of the image. Noise level is an important parameter of image processing optimization algorithms such as image denoising, image compression, image stitching, etc. Therefore, it is very important to accurately estimate the noise level for image denoising. With the development of CMOS image sensors, in order to reduce the influence of noise, some manufacturers directly embed a noise reduction module in the image sensor chip, which can effectively suppress the noise that has nothing to do with the s...

AI Technical Summary

Problems solved by technology

However, existing density peak clustering algorithms tend to select cluster centers in dense areas, and tend to ignore clusters in sparse areas.

For data sets with large density differences, the DPC algorithm often judges the density points in the sparse area as outliers or misplaces them to adjacent dense clusters, thereby ignoring the cluster centers of the sparse clusters, resulting in inaccurate clustering results. sex

Some existing DPC-based improved clustering algorithms often introduce k-nearest neighbors (KNN) to improve the clustering effect, but the parameter k is a value set subjectively based on past experience, and the size of the k value often affects the clustering effect. The result will have a large impact, which further affects the accuracy of the clustering

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