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 relates to the technical field of image registration methods, in particular to a binarized image registration method based on improved structural similarity. Background technique [0002] The registration method based on the pixel gray level generally does not require complex preprocessing of the image, but uses some statistical information of the gray level of the image itself to measure the similarity of the image. Commonly used measurement functions include mean square and error, Correlation coefficient and (normalized) mutual information, etc. Mutual information was proposed by Viola et al. and Collignon et al. in 1995. As a registration measurement function, it has become one of the research hotspots in recent years. The algorithm can also achieve sub- Pixel-level registration, but local extrema can lead to unstable registration, especially for multimodal image registration. [0003] The structural similarity proposed by Zhou Wang a...

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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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