IMAGE NOISE REDUCTION USING LUCAS KANADE INVERSE ALGORITHM

摘要

A reduced noise image can be formed from a set of images. One of the images of the set can be selected to be a reference image and other images of the set are transformed such that they are better aligned with the reference image. A measure of the alignment of each image with the reference image is determined. At least some of the transformed images can then be combined using weights which depend on the alignment of the transformed image with the reference image to thereby form the reduced noise image. By weighting the images according to their alignment with the reference image the effects of misalignment between the images in the combined image are reduced. Furthermore, motion correction may be applied to the reduced noise image.

主权项

1. A method of transforming a first image to bring it closer to alignment with a second image, the method comprising:
implementing a multiple kernel tracking technique to determine positions of a set of candidate regions of the first image based on a similarity between a set of target regions of the second image and the set of candidate regions of the first image, wherein the target regions of the second image are respectively positioned over the positions of a predetermined set of points of the second image; using at least some of the determined positions of the set of candidate regions to initialize a Lucas Kanade Inverse algorithm; using the Lucas Kanade Inverse algorithm to determine a set of points of the first image which correspond to at least some of the predetermined set of points of the second image; determining parameters of a transformation to be applied to the first image based on an error metric which is indicative of an error between a transformation of at least some of the determined set of points of the first image and the corresponding points of the predetermined set of points of the second image; and applying the transformation to the first image to bring it closer to alignment with the second image.