Image registration method based on maximum stable extreme region and phase coherence

Abstract

The invention discloses an image registration method based on a maximum stable extreme region and phase coherence, and aims at solving the defects of low repeating rate of extracted characteristic points and large operation complexity in the prior art. The method comprises the steps of 1, inputting two images with affine transformation, and respectively performing detection and matching for the maximum stable extreme region; 2, fitting the matching areas of the two images, and amplifying and normalizing; 3, performing band-pass decomposition for two normalized areas; 4, detecting the characteristics points based on the maximum phase coherence matrix, and constructing the probability distribution of the detected characteristics points; 5, estimating the accurate affine transformation matrix between two point sets; 6, estimating the transformation matrix of the two images according to the two normalized areas; 7, calculating the accurate affine transformation matrix between the two images, and finishing the image registration. According to the method, the characteristics points with relatively high repeating rate and accurate matching rate can be extracted, the calculation efficiency can be increased, and the image fusion, image splicing and three-dimensional reconstruction can be performed.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an affine transformation image registration method, which can be applied to the fields of image fusion, image splicing, three-dimensional reconstruction and the like. Background technique [0002] In the fields of image fusion, image stitching and 3D reconstruction, it is necessary to register multiple views of the same scene. In general, feature-based image registration methods can be used for image registration, mainly because some image features are invariant to image scale and rotation, and it is computationally efficient to use only feature information to find the geometric relationship between images high merit. However, when there is a large affine transformation between two images, it is often difficult to extract features with high repetition rate or precise position, which leads to the problem of insufficient registration accuracy or even failure t...

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Problems solved by technology

The disadvantage of this method is that because the method simulates the image in the affine space to form images from various perspectives, it consumes a lot of memory, and at the same time, it also introduces a large number of wrong matching points when extracting a large number of correct matching points. , and to obtain higher image registration accuracy, a more complex optimization process is required to delete the mismatched points, which undoubtedly increases the computational complexity

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