Image denoising method based on high-order overlapping group sparse total variation

Abstract

The invention provides an image denoising method based on high-order overlapping group sparse total variation, and provides an overlapping group sparse total variation image recovery method based on high-order regularization term constraint on the basis of a traditional first-order overlapping group sparse total variation technology. According to the first-order overlapping combination technology,a total variation gradient of each conventional pixel is popularized into a combination gradient, so that the difference between a smooth region and an edge region is improved, and a high-order regular term can more effectively alleviate a'step effect 'from second-order or higher-order gradient information, so that the protection on the edge of the image is improved. In order to improve the operation speed of image restoration, transverse and longitudinal differential matrix operation of an image is modeled as convolution operation, periodic boundary conditions are combined, so that two-dimensional fast Fourier transform is skillfully applied to an image restoration problem, and point multiplication operation on a frequency domain is used for replacing large matrix operation on an airspace.

Description

technical field [0001] The invention relates to the field of image processing, in particular to an image denoising method based on high-order overlapping group sparse total variation. Background technique [0002] Since the Total Variation (TV) model was proposed, it has been widely used in various image processing problems such as image denoising because of its good processing effect. However, this model also has obvious shortcomings, that is, the "ladder effect" is relatively serious. [0003] Since the proposal of the total variational denoising model, various improved models aimed at removing its "step effect" have been proposed successively, such as anisotropic total variation (anisotropic TV, ATV), fractional total variation (fractional order TV, FTV), nonlocal TV (NLTV), overlapping groups parsity TV (OGSTV), and total generalized variation (TGV) models. [0004] Traditional image denoising methods usually use first-order anisotropic total variation methods. Compar...

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Problems solved by technology

Compared with the basic full variational model, although various improved full variational models have some improvements in overcoming the "step effect", there is still room for improvement.

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