  Bokeh is an important artistic effect used to highlight the main object of
interest on the photo by blurring all out-of-focus areas. While DSLR and system
camera lenses can render this effect naturally, mobile cameras are unable to
produce shallow depth-of-field photos due to a very small aperture diameter of
their optics. Unlike the current solutions simulating bokeh by applying
Gaussian blur to image background, in this paper we propose to learn a
realistic shallow focus technique directly from the photos produced by DSLR
cameras. For this, we present a large-scale bokeh dataset consisting of 5K
shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR with
50mm f/1.8 lenses. We use these images to train a deep learning model to
reproduce a natural bokeh effect based on a single narrow-aperture image. The
experimental results show that the proposed approach is able to render a
plausible non-uniform bokeh even in case of complex input data with multiple
objects. The dataset, pre-trained models and codes used in this paper are
available on the project website.
