  The paper addresses the image fusion problem, where multiple images captured
with different focus distances are to be combined into a higher quality
all-in-focus image. Most current approaches for image fusion strongly rely on
the unrealistic noise-free assumption used during the image acquisition, and
then yield limited robustness in fusion processing. In our approach, we
formulate the multi-focus image fusion problem in terms of an analysis sparse
model, and simultaneously perform the restoration and fusion of multi-focus
images. Based on this model, we propose an analysis operator learning, and
define a novel fusion function to generate an all-in-focus image. Experimental
evaluations confirm the effectiveness of the proposed fusion approach both
visually and quantitatively, and show that our approach outperforms
state-of-the-art fusion methods.
