Binarization image registration method based on improved structural similarity

Abstract

The invention provides a binarization image registration method based on improved structural similarity, which adopts the following steps: firstly, a binarization image is obtained through converting a reference image and a floating image into a binary image; secondly, a new binary image is obtained in the way that the floating image is subject to geometric transformation based on a coarse registration parameter after coarse registration is performed; thirdly, utilizing a Powell optimization algorithm and taking the improved structural similarity as a registration measure function, the fine registration is performed; and finally, the new floating image is subject to spacial geometric transformation based on the parameter obtained through fine registration, and then the transformed image is fused with the binarization reference image, thereby displaying the registration result. According to the invention, the conventional defining formula of the structural similarity function is improved, and the improved function is introduced to the binzrization image registration for the first time. Therefore, the invention provides the algorithm which is comparatively universal and has good robustness, and can reach the pixel registration.

Description

Technical field [0001] The invention involves the technical field of image distribution method, which specializes in a dual -value image standard method based on improved structural similarity. Background technique [0002] Based on pixel grayscale, generally do not need to be complicated to pre -processing the image, but use some statistical information of the grayness of the image itself to measure the similarity of the image.The correlation coefficient and (regulatory) mutual information, etc., the mutual information was proposed by Viola et al.Pixel -level allocation, but the local extreme value will lead to unstable allocation, especially the multi -mode image allocation. [0003] Processing, 2004, 13 (4): 600-612.), used to evaluate image quality, such as quality assessment after images to noise. [0004] Structural similarity model is based on the three parts of the image brightness, contrast, and structural information of the image, which is defined as: [0005] [0006...

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Problems solved by technology

[0014] On the other hand, although the structural similarity function proposed by Zhou Wang and Alan C. Bovik et al requires , 1=0.01, K2=0.03 (see the Chinese invention patent application with publication number CN102169576A), and the graphic registration experiment proves that if K1>0.000001, K2>0.000003, when used for binarized image registration, it is easy to fall into local extremum, which makes the registration fail

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