A Fuzzy Kernel Estimation Method Based on l0 and l1 Regular Terms

Abstract

The invention provides a fuzzy kernel estimation method based on L0 and L1 regular terms. The L0 regular term is used as the constraint condition when the middle clear image is solved, and the ringingeffect is effectively inhibited. The L1 regular term is used as the regularization constraint condition during estimation of the fuzzy kernel, so that the estimation fuzzy kernel is sufficiently sparse. According to the established fuzzy kernel estimation model, the fuzzy kernel estimation method based on the L0 and L1 regular terms is applied to an image pyramid theory and is used for solving anestimation fuzzy kernel from the top layer of the image pyramid to the bottom layer in a coarse-to-fine mode. The obtained fuzzy kernel is used for an image non-blind restoration algorithm, and a final clear image is restored. The fuzzy kernel of the blurred image can be accurately estimated, so that a fuzzy kernel of the estimated fuzzy kernel image is used, and an initial fuzzy kernel image isrestored to obtain a final clear image.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a blur kernel estimation method based on L0 and L1 regular terms. Background technique [0002] Ideally, the photos taken by the camera equipment are clear and ideal images, but the images are often affected by various other factors during the imaging process, such as camera shake, the relative distance between the camera equipment and the shooting target, etc. Movement, etc., resulting in the degradation of the quality of the captured image, this process is usually referred to as the image degradation process. The image degradation process will not only lead to a serious decline in the visual effect of the image, but also greatly reduce the practical application value of the image. [0003] Image restoration is to follow the inverse process of image restoration and use the prior knowledge of the image degradation process to obtain a clear image. Although the causes of image deg...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problem that a single first-order regularization constraint is imposed on the image at present, or the blur kernel estimation is not performed according to the scale of the image, resulting in the inability to accurately estimate the blur kernel, the present invention provides a blurring method based on L0 and L1 regularization terms Kernel estimation methods, including:

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