Method and apparatus for stitching dual-camera images and electronic device

An apparatus for stitching dual-camera images, comprising a processor and a memory, wherein the memory stores instructions executable by the processor, and the processor is configured to:

• collect two images having overlapping photographic content;
• extract and match feature points of the two images;
• roughly align the two images by using a homography matrix;
• and
• accurately align the two images by using a least squares method to complete stitching the two images,
• wherein the processor is configured to:
• assume that a feature point set of an image A of the two images is P, a feature point set of an image B of the two images is Q, N pairs of matching feature points exist between feature point set P and feature point set Q, and feature points pi(xi, yi) and qj (xj, yj) belong to feature point set P and feature point set Q respectively and match each other, then calculate an Euclidean distance between pi(xi, yi) and qj(xj, yj) by using the following formula:
• distpq=∥pi−qj∥=√{square root over ((xi−xj)2+(yi−yj)2)} (10),
• wherein i is an integer and satisfies 0<i≤N, and j is an integer and satisfies 0<j≤K, wherein K denotes a number of feature points in the feature point set Q;
• calculating an error by using the following formula:
• error=Σi=1N|(Rpi+T)−qj|2 (11)
• wherein R denotes a rotation matrix and T denotes a translation matrix;
• and
• finding an optimal solution to the formula (11) by using the least squares method,
• wherein the optimal solution is the rotation matrix R and the translation matrix T with a smallest value of the error, and mapping the image A onto the image B through the rotation matrix R and the translation matrix T with the smallest value of the error.

A non-transitory computer-readable storage medium storing computer instructions for causing a processor to execute a method for stitching dual-camera images, comprising:

• collecting two images having overlapping photographic content;
• extracting and matching feature points of the two images;
• roughly aligning the two images by using a homography matrix;
• and
• accurately aligning the two images by using a least squares method to complete stitching the two images;
• wherein accurately aligning the two images by using the least squares method comprises:
• assuming that a feature point set of an image A of the two images is P, a feature point set of an image B of the two images is Q, N pairs of matching feature points exist between feature point set P and feature point set Q, and feature points pi(xi, yi) and qj(xj, yj) belong to feature point set P and feature point set Q respectively and match each other, then calculating an Euclidean distance between pi(xi, yi) and qj(xj, yj) by using the following formula:
• distpq=∥pi−qj∥=√{square root over ((xi−xj)2+(yi−yj)2)} (10),
• wherein i is an integer and satisfies 0<i≤N, and j is an integer and satisfies 0<j≤K, wherein K denotes a number of feature points in the feature point set Q;
• calculating an error by using the following formula:
• error=Σi=1N|(Rpi+T)−qj|2 (11),
• wherein R denotes a rotation matrix and T denotes a translation matrix;
• and
• finding an optimal solution to the formula (11) by using the least squares method, wherein the optimal solution is the rotation matrix R and the translation matrix T with a smallest value of the error, and mapping the image A onto the image B through the rotation matrix R and the translation matrix T with the smallest value of the error.