A novel nonuniformity correction algorithm based on speeded up robust features extraction

The traditional image-registration based non-uniformity correction algorithm assumes that the displacements of inter-frame images are horizontal or vertical which ignores rotation, affine transformation, etc. This restriction results in low registration accuracy and then depresses the quality of nonuniformity correction. In this paper, we bring forward a novel nonuniformity correction algorithm based on speeded up robust features extraction (SURF). The feature points are extracted by hessian operator in the algorithm and are described in use of the 64-dimensional descriptor. Then the algorithm gets all the matching pairs by matching threshold, and all matching pairs can be used to estimate transform converting relations between the two consecutive images. The converting relations combined with the steepest gradient descent method can adaptively correct the gain and offset calibration parameters, which can get the accurate registration including rotation and affine transformation, and then reduce excessive noise and ghost artifacts in the process of correction. The experiments also show that our algorithm can solve the registration accuracy problem in complex conditions, and improve the correction results effectively. (C) 2015 Elsevier B.V. All rights reserved.