Efficient misalignment-robust multi-focus microscopical images fusion

Abstract

In this paper we propose a very efficient method to fuse the unregistered multi-focus microscopical images based on the speed-up robust features (SURF). Our method follows the standard pipeline of first registration and then fusion. However, instead of treating the registration and fusion as two completely independent stages, we propose to reuse the determinant of the approximate Hessian generated in SURF detection stage as the corresponding salient response for the final image fusion, thus it enables nearly cost-free saliency map generation. In addition, due to the adoption of SURF scale space representation, our method can generate scale-invariant saliency map which is desired for scale-invariant image fusion. We present an extensive evaluation on the dataset consisting of several groups of unregistered multi-focus 4K ultra HD microscopic images. Compared with the state-of-the-art multi-focus image fusion methods, our method is much faster and gains competitive or even better performance both in terms of the visual quality and objective metrics. Our method provides an efficient way to integrate complementary and redundant information from multiple multi-focus high-resolution unregistered images into a fused image that contains better description than any of the individual input images.