An automatic image fusion algorithm for unregistered multiply multi-focus images

Abstract

The multi-focus image fusion technique provides a promising way to extend the depth of defocused images by combining multiple images with diverse focuses into a single focused one. In this paper, we present a robust and automated algorithm for the fusion of unregistered multiply multi-focus images. The motivation of our method lies in the fact that the source images are assumed to be perfectly aligned in the majority of previous research. Actually, the assumption is difficult to achieve in many practical situations. Hence, image registration method for multi-focus images is talked in this paper. We choose a multi-focus image as reference one in the image registration process by entropy theory. Speeded Up Robust Features (SURF) feature detector with Binary Robust Invariant Scalable Keypoints (BRISK) feature descriptor is used in the feature matching process. An improved RANdom Sample Consensus (RANSAC) algorithm is adopted to reject incorrect matches. The registered images are fused using stationary wavelet transform (SWT) with sym5 wavelet basis. The experimental results prove that the proposed algorithm achieves better performance for unregistered multiply multi-focus images, and it is especially robust to scale and rotation translation compared with traditional direct fusion method.