Norm ratio regularization based rapid image blind deblurring method

Abstract

The invention discloses a norm ratio regularization based rapid image blind deblurring method. The norm ratio regularization based rapid image blind deblurring method comprises estimating a blurring kernel through a multi-scale method with a regularization item of a norm specific value 11 to 2 to 12 serving as priori knowledge and rapidly restoring an original sharply focused image u in high quality through a non-blind image deconvolution method of an enclosed threshold value formula according to an obtained blurring kernel k matrix. According to the norm ratio regularization based rapid image blind deblurring method, the blurring kernel solving process is gradually performed from a coarse scale to a fine scale and an algorithm with multiple scales guarantees the accuracy and the robustness of calculation of a blurring kernel function; the recovering result is accurate, the calculation efficiency is high, and the perform is superior to a traditional algorithm due to the fact that a prior model with the norm ratio is more close to graded distribution of natural images; the energy during solving is enabled to be decreasing due to the fact that the norm ratio regularization prior serves as the Smooth; the sharp images can be rapidly obtained in high quality through non-blind image deconvolution method of the enclosed threshold value formula after the blurring kernel is estimated.

Description

technical field [0001] The invention relates to computer image processing technology, in particular to a fast image blind deblurring method based on norm ratio regularization. Background technique [0002] The blurring of the image is generally caused by camera movement, hand shaking, etc. during the acquisition process. We restore a clear image through the known blurred image information. Image blur can be classified according to the nature of the blur kernel: Blind Image Deconvolution (BID, blind deconvolution) and Non-Blind Image Deconvolution (NBID, non-blind deconvolution). BID is to restore a clear image when the blur kernel is unknown. In this case, there is no other information except the collected blur image. NBID restores a clear original image when the blur kernel is known. With the very important information of the blur kernel, the work of deconvolution is relatively easier. The main task is how to maintain the details. suppress noise. In general, NBID is the ...

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Problems solved by technology

Although l 0 The regularization prior is able to find sparse solutions, but since l 0 The discontinuity and non-convexity of the norm make the solution process difficult, and the l 1 regularized to approximate l 0 regularization, and is widely used in image processing, although l 1 The norm has achieved very good results, but later Fergus found that the gradient of the clear image obeys the long-tail distribution by observing the gradient distribution in the clear image scene. There have been many studies on how to further describe this long-tail distribution with a mathematical model. The common ones are Gaussian distribution model, mixed Gaussian distribution model, Laplace model, etc., but the realization of these algorithm models has certain complexity, low computational efficiency, and poor restoration effect

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