（1）background：
Image registration： the process of overlaying two or more images of the same scene taken at different times from different viewpoints, and/or by different sensors. It geometrically aligns two images—the reference and sensed images
Chinese Interpretation: image registration is to use some method, based on some evaluation criteria, the one or more decks picture (partial) optimal mapping onto a target picture.
Dense image correspondence: correspondence estimation is a task of matching pixels of one image with those of others; when referring to dense correspondence estimation,the emphasis is on finding suitable matches(correspondences) for every one those pixels;
（2）Method：
The image registration method based on feature
Feature detection：
Salient and distinctive objects(closed-boundary regions, edges, contours, line intersections, corners, etc.) are manually or, preferably, automatically detected. For further processing, these features can be represented by their point representatives (centers
of gravity, line endings, distinctive points), which are called control points (CPs) in the literature.
Feature matching：
In this step, the correspondence between the features detected in the sensed image and those detected in the reference image is established.
Various feature descriptors and similarity measures along with spatial relationships among the features are used for that purpose
Transform model estimation.
The type and parameters of the so-called mapping functions, aligning the sensed image with the reference image, are estimated. The parameters of the mapping functions are computed by means of the established feature correspondence.
Image resampling and transformation
the sensed image is transformed by means of the mapping functions.image values in non-integer coordinates are computed by the appropriate interpolation technique.
references:
【1】Armin M A, Barnes N, Khan S, et al. Unsupervised Learning of Endoscopy Video Frames’ Correspondences from Global and Local Transformation[M]//OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis. Springer, Cham, 2018: 108-117.
[2] Column know almost https://zhuanlan.zhihu.com/p/62210477
【3】Image registration methods: a survey