Image restoration method based on overlapping combination sparse high-order total variation

Abstract

The invention discloses an image restoration method based on overlapping combination sparse high-order total variation. The method comprises the following steps: S30, establishing an image restorationhybrid model of an original image; S40, obtaining a constrained optimization problem; S50, splitting the constrained optimization problem into independent sub-problems to obtain an augmented Lagrangefunction; S60, solving each variable quantum problem of the augmented Lagrange function by adopting a Lagrange multiplier in the kth iteration process to obtain a target function in the kth iterationprocess, and further obtaining a restored image, a first variable parameter and a second variable parameter in the kth iteration process; S70, updating the Lagrange multiplier according to the restored image, the first variable parameter and the second variable parameter to obtain a Lagrange multiplier in the next iteration process; and S80, judging whether the stop parameter in the kth iterationprocess is less than or equal to a stop threshold, if yes, determining a restored image, and if not, setting k to be equal to k + 1, and returning to execute the step S60.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image restoration method based on overlapping combination sparse high-order total variation. Background technique [0002] In the information age, image information is of great significance. However, images are often polluted by noise during the process of acquisition, transmission, processing and recording, which will cause image degradation and quality degradation. Therefore, image restoration is an important research content in image processing. [0003] The image after the traditional total variation restoration obviously has a ladder effect, and the details such as the edge of the image cannot be well preserved. On this basis, many algorithms are sequentially proposed to suppress the staircase effect. One way is to replace the traditional total variation with a higher-order total variation, such as a high-order total variation. Although this method can use the...

AI Technical Summary

Problems solved by technology

One way is to replace the traditional total variation with a higher-order total variation, such as a high-order total variation. Although this method can use the high-order derivative information in all directions of the image to build a model, the calculation time is greatly increased.

Another way to suppress the ladder effect is to extend the gradient information at the pixel level to the overlapping combination sparse gradient information, such as the full variation model based on the overlapping combination sparse regularization term, which makes full use of the gradient between the pixel neighborhoods Information, to a certain extent reduces the ladder artifact information, but still has a large amount of calculation

It can be seen that the traditional image restoration method has the problem of large amount of calculation, which easily affects the efficiency of image restoration

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