  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 pipeline of first registration and then fusion. However,
instead of treating the registration and fusion as two completely independent
stage, 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 with
size of 4112 x 3008. Compared with the state-of-the-art multi-focus image
fusion methods, our method is much faster and achieve better results in the
visual performance. Our method provides a flexible and efficient way to
integrate complementary and redundant information from multiple multi-focus
ultra HD unregistered images into a fused image that contains better
description than any of the individual input images. Code is available at
https://github.com/yiqingmy/JointRF.
