Abstract - In order to overcome the limited depth of field of usual photographic devices, a common approach is multi-focus image fusion (MFIF). From a stack of images acquired with different focus settings, these methods aim at fusing the content of the images of the stack to produce a final image that is sharp everywhere. Such methods can be very efficient, but when a global geometric alignment of images is out-of-reach, or when some objects are moving, the final image shows ghosts or other artefacts. In this paper, we propose a generic method to overcome these limitations. We first select a reference image, and then, for each image of the stack, reconstruct an image that shares the geometry of the reference and the sharpness content of the image at hand. The reconstruction is achieved thanks to a specially crafted modification of the PatchMatch algorithm, adapted to blurred images, and to a dedicated postprocessing for correcting reconstruction errors. Then, from the new image stack, MFIF is performed to produce a sharp result. We show the efficiency of the result on a database of challenging cases of hand-held shots containing moving objects.

The top row images present a sequence of multifocus images with object motion. The reference image is the right-most image.
The bottom image corresponds to the fusion with the proposed method, NL-MFIF.