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

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...

AI Technical Summary

Problems solved by technology

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

Images