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Non-local-total-variation image restoration method based on sparse overlapped group priori constraints

A non-local, total variational technology, applied in the field of image restoration, can solve the problem that the image structure cannot be restored accurately, and achieve the effect of avoiding inner loop calculation, making up for calculation redundancy, and improving recognition

Inactive Publication Date: 2018-05-04
TIANJIN NORMAL UNIVERSITY
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In addition, since non-local total variation restores image details by combining the variational framework and non-local self-similarity constraints, similar image structures cannot be accurately restored if only non-local self-similarity is used as the only constraint. There are certain limitations

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  • Non-local-total-variation image restoration method based on sparse overlapped group priori constraints
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  • Non-local-total-variation image restoration method based on sparse overlapped group priori constraints

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[0031] The technical solutions of the present invention will be further described below in conjunction with specific embodiments.

[0032] Step 1. Establish a mathematical model of the blurred image degradation process. Under the linear invariant system, the image degradation process can usually be described as the convolution of the original image and the blur kernel, as shown in the attached figure 2 As shown, g, f and h represent the PSF (Point Spread Function) of the blurred image, the original image and the degradation model respectively, n is the additive noise, assuming that the degradation system is a linear space invariant system, the mathematics of the degradation process Manifested as

[0033] g=h*f+n (1)

[0034] Among them, h is determined by the fuzzy parameters. If h and g are known, f can be solved by deconvolution to obtain the restored image.

[0035] Step 2. In order to improve the ill-conditionedness of the deconvolution operation in step 1, construct a...

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Abstract

The invention discloses a non-local-total-variation image restoration method based on sparse overlapped group priori constraints. The method includes the following steps that 1, a degenerative-processmathematical model g=h*f+n is established; 2, a constraint item and a regularization term are established, and are the image representation constraint item phi<OGS>(f) and the image non-local-total-variation regularization term phi<NLTV>(f) under the sparse overlapped group priori constraints respectively; 3, an image restoration minimum energy functional model arg min lambada phi<fid>(g, f)+alpha phi<OGS>(f)+phi<NLTV>(f) is established; 4, a target function is optimized with the ADMM algorithm, and a middle restored image is solved and a updated. The end condition is met, iteration is end, and a final restored image is obtained. According to the method, image priori information can be fully used in the introduced sparse-overlapped-group-priori image representation constraint item, the identifying degree of the image similar structure is increased, the non-local-total-variation regularization defect is overcome, and more detail information is further reserved.

Description

technical field [0001] The invention belongs to the technical field of image restoration, and in particular relates to a non-local total variation image restoration method based on sparse overlapping group prior constraints. Background technique [0002] Image is an important medium for people to obtain and record information. However, in the imaging process, due to factors such as the inaccurate focus of the camera itself, relative motion and noise between the camera and the target during the shooting process, the quality of the image is greatly affected. The process is called the degradation of the image. The degradation of the image is quite unfavorable to the further application of the image such as feature extraction, target recognition and image analysis. Therefore, it is necessary to introduce image restoration technology to restore clear images rich in detailed information from degraded blurred images. Especially for some special image acquisition occasions, many sce...

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/00G06T5/73
Inventor 石明珠高静韩婷婷
Owner TIANJIN NORMAL UNIVERSITY
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