Image registration method based on improved structural similarity

Abstract

The invention provides an image registration method based on improved structural similarity. According to the invention, the improved structural similarity serves as the objective function of the image registration for the first time; four parameters of the two-dimensional image rigid body transformation are obtained through translation, rotation and consistent scaling along the X-axis and Y-axis; and the single-modal and multimodal images are analyzed in detail based on the registration algorithm and performance of the structural similarity and are compared with that based on a normalized mutual information registration algorithm. The result shows that when an absolute value is extracted during defining the structural similarity, the structural similarity has favorable features of a convex function; for either the single-modal image registration or the multimodal image registration, the structural similarity serving as the measure function can achieve the sub-pixel registration with registration precision and robustness better than that based on the classic normalized mutual information registration algorithm; and if K1 is less than or equal to 0.000001, and K2 is less than or equal to 0.000003, the two-value image can achieve the pixel registration.

Description

technical field [0001] The invention relates to an image registration method, in particular to an image registration method based on improved structural similarity. Background technique [0002] As a necessary step before image analysis, Image Registration (Images Registration) is an important technology in the field of image processing. The spatial geometric transformation is carried out on each image, so that the contents of the image correspond topologically and align geometrically. [0003] The registration framework consists of four parts: geometric transformation (Transform), image interpolation (Image Interpolator), similarity measure (Similarity Metric), function optimization (Cost Function Optimizer), the most critical of which is to determine the similarity measure (Similarity Metric) , which directly affects the registration effect. According to the image information used in image registration, similarity measures are divided into three categories, including im...

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Problems solved by technology

[0016]If you directly use the structural similarity SSIM proposed by Zhou Wang and Alan C. Bovik et al. as the objective function of image registration (Cost Function Optimizer), you can only match Quasi-single-modal images, but cannot solve the problem of multi-modal image registration. This is because for multi-modal images, due to the large difference in image content, even if they are fully aligned, SSIM is generally not equal to 1. In SSIM drops instead

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