Image restoration method suitable for neutron imaging system image and based on Gauss-Poisson hybrid noise model

Abstract

The invention discloses an image restoration method suitable for a neutron imaging system image and based on a Gauss-Poisson hybrid noise model. According to the method, an image with Gauss-Poisson hybrid noise is transformed into a Gaussian white noise image through GAT transformation, a regional limitation BM3D algorithm is used to process the image obtained after transformation, and then unbiased GAT inverse transformation is performed on the obtained image to obtain a final de-noised image. Through the method, a Lloyd algorithm is used to perform regional division on the image, similar blocks of BM3D Stage2 are matched and limited in the same region, and the problems that through a traditional BM3D method, when Gaussian noise variance is large, object edge information is seriously lost in a filtering process, and an object edge in an image after de-noising is fuzzy are solved. Compared with a traditional BM3D algorithm, through the de-noising method, a restored image, with a better visual effect, of an image subjected to strong Gauss-Poisson hybrid noise interference can be obtained, object edge information is reserved more completely, and a higher PSNR value is obtained.

Description

Technical field [0001] The invention belongs to the technical field of information processing and relates to an image noise reduction method. Background technique [0002] Neutron imaging technology is an effective tool for non-destructive testing and many other basic research fields. In the mobile neutron imaging system, due to up to 10 9 With n / s neutron generator, alignment system less than 15m, slow neutron flow and limited length / diameter ratio, the resulting image is usually accompanied by severe Gaussian Poisson mixed noise. Image noise will cause: ①The subjective visual effect of the image is poor; ②The image features are masked, which reduces the image quality and accuracy, and affects the later work of image feature extraction and analysis. Therefore, for the neutron imaging system, image noise reduction is a crucial post-processing process. [0003] The image information is mainly divided into two parts: high frequency and low frequency. The high frequency part is the ...

AI Technical Summary

Problems solved by technology

In the traditional BM3D noise reduction method, due to the use of global similar block matching, the similar block matching effect is poor. Under the interference of Gaussian noise with high variance, the filtering process often causes the object edge and noise in the image to be used as high-frequency information together. Cut or remove, resulting in severe blurring of the edge of the object, resulting in poor final noise reduction effect

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