Regularization fuzzy kernel estimation method based on mixed order L0

Abstract

The invention provides a regularization fuzzy kernel estimation method based on the mixed order L0. The method is characterized by comprising the steps of subjecting an intermediate clear image in a fuzzy kernel estimation model to the regularization constraint at the mixed order L0, protecting the edge of the image by utilizing the first-order constraint, inhibiting the ringing effect generated by the first-order constraint by utilizing the second-order constraint, restoring a clear intermediate image, adding an improved adaptive adjustment factor in the fuzzy kernel estimation model, and extracting more obvious edge information beneficial to fuzzy kernel estimation from the intermediate clear image. According to the technical scheme of the invention, based on the semi-quadratic variable splitting technology, a proposed fuzzy kernel estimation model can be solved. Meanwhile, the experimental results of artificial blurred images and real blurred images prove that, the fuzzy kernel estimation method provided by the invention is effective and restored images are obviously improved in subjective visual effect and objective evaluation index compared with most representative methods in recent years.

Description

technical field [0001] The present invention relates to an image processing method, in particular to a method based on the mixing order L 0 Regularized Blur Kernel Estimation Method. Background technique [0002] In the process of image acquisition, transmission, and storage, due to the influence of factors such as physical defects of the imaging equipment itself, changes in the external environment, and improper operation of the operator, it is inevitable that the image will be degraded to varying degrees. Not only seriously affect the visual effect of the image, but also greatly reduce the practical application value. As a result, image restoration technology emerged as the times require, and has been widely used in astronomical observation, medical imaging, video multimedia, criminal investigation and other fields. At present, many image restoration methods require more prior information, or have disadvantages such as poor effect and high algorithm complexity. For this...

AI Technical Summary

Problems solved by technology

However, when the image contains rich details or a large blur scale, the blur kernel estimated by the current method is generally not ideal, and the image restored from the inaccurate blur kernel will naturally produce different degrees of ringing effect

[0005] Through analysis, when the image details are rich or the blur scale is large, the existing processing techniques are: 1) impose a single first-order regularization constraint on the image, resulting in an accurate estimation of the blur kernel; 2) according to the magnitude of the image edge instead of If the scale is used to estimate the blur kernel, it is easy to misjudge the details of the image as the edge of the image, resulting in inaccurate blur kernel estimation

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