A Computationally Efficient Algorithm for Multi-Focus Image Reconstruction

A method for synthesizing enhanced depth of field digital still camera pictures using multiple differently focused images is presented. This technique exploits only spatial image gradients in the initial decision process. The spatial gradient as a focus measure has been shown to be experimentally valid and theoretically sound under weak assumptions with respect to unimodality and monotonicity. Subsequent majority filtering corroborates decisions with those of neighboring pixels, while the use of soft decisions enables smooth transitions across region boundaries. Furthermore, these last two steps add algorithmic robustness for coping with both sensor noise and optics-related effects, such as misregistration or optical flow, and minor intensity fluctuations. The dependence of these optical effects on several optical parameters is analyzed and potential remedies that can allay their impact with regard to the technique’s limitations are discussed. Several examples of image synthesis using the algorithm are presented. Finally, leveraging the increasing functionality and emerging processing capabilities of digital still cameras, the method is shown to entail modest hardware requirements and is implementable using a parallel or general purpose processor.