  Bokeh effect is used in photography to capture images where the closer
objects look sharp and every-thing else stays out-of-focus. Bokeh photos are
generally captured using Single Lens Reflex cameras using shallow
depth-of-field. Most of the modern smartphones can take bokeh images by
leveraging dual rear cameras or a good auto-focus hardware. However, for
smartphones with single-rear camera without a good auto-focus hardware, we have
to rely on software to generate bokeh images. This kind of system is also
useful to generate bokeh effect in already captured images. In this paper, an
end-to-end deep learning framework is proposed to generate high-quality bokeh
effect from images. The original image and different versions of smoothed
images are blended to generate Bokeh effect with the help of a monocular depth
estimation network. The proposed approach is compared against a saliency
detection based baseline and a number of approaches proposed in AIM 2019
Challenge on Bokeh Effect Synthesis. Extensive experiments are shown in order
to understand different parts of the proposed algorithm. The network is
lightweight and can process an HD image in 0.03 seconds. This approach ranked
second in AIM 2019 Bokeh effect challenge-Perceptual Track.
