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In this paper we propose a very efficient method to fuse the unregisteredmulti-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 independentstage, we propose to reuse the determinant of the approximate Hessian generatedin SURF detection stage as the corresponding salient response for the finalimage fusion, thus it enables nearly cost-free saliency map generation. Inaddition, due to the adoption of SURF scale space representation, our methodcan generate scale-invariant saliency map which is desired for scale-invariantimage fusion. We present an extensive evaluation on the dataset consisting ofseveral groups of unregistered multi-focus 4K ultra HD microscopic images withsize of 4112 x 3008. Compared with the state-of-the-art multi-focus imagefusion methods, our method is much faster and achieve better results in thevisual performance. Our method provides a flexible and efficient way tointegrate complementary and redundant information from multiple multi-focusultra HD unregistered images into a fused image that contains betterdescription than any of the individual input images. Code is available athttps://github.com/yiqingmy/JointRF.

Efficient Misalignment-Robust Multi-Focus Microscopical Images Fusion