1. A method, comprising: applying a first grid to a blurred first image; computing a determinant of a Hessian matrix at predetermined points of the first grid on the blurred first image; determining low resolution feature points in the blurred first image based on the first grid; applying a second grid to the blurred first image, the second grid being finer than the first grid; computing a determinant of a Hessian matrix at predetermined points of the second grid on the blurred first image; determining high resolution feature points in the blurred first image based on the second grid; creating a first set of blurred first image key points; extracting a first feature descriptor for each of the blurred first image key points; applying the first grid to a blurred second image; computing a determinant of a Hessian matrix at predetermined points of the first grid on the blurred second image; determining low resolution feature points in the blurred second image based on the first grid; applying the second grid to the blurred second image; computing a determinant of a Hessian matrix at predetermined points of the second grid on the blurred second image; determining high resolution feature points in the blurred second image based on the second grid; creating a second set of blurred second image key points extracting a second feature descriptor for each of the blurred second image key points; selecting blurred first image key points and blurred second image key points for matching based on a measure of closeness of the first feature descriptors and the second feature descriptors; and mapping the first image into the second image based on matching pairs of blurred first image key points and blurred second image key points.