A shallow depth-of-field is often used as a creative element in photographs. This, however, comes at the cost
of expensive and heavy camera equipment, such as large sensor DSLR bodies and fast lenses. In contrast,
cheap small-sensor cameras with fixed lenses usually exhibit a larger depth-of-field than desirable. In this case
a computational solution is suggesting, since a shallow depth-of-field cannot be achieved by optical means. One
possibility is to algorithmically increase the defocus blur already present in the image. Yet, existing algorithmic
solutions tackling this problem suffer from poor performance due to the ill-posedness of the problem: The amount
of defocus blur can be estimated at edges only; homogeneous areas do not contain such information. However,
to magnify the defocus blur we need to know the amount of blur at every pixel position. Estimating it requires
solving an optimization problem with many unknowns.
We propose a faster way to propagate the amount of blur from the edges to the entire image by solving the
optimization problem on a small scale, followed by edge-aware upsampling using the original image as guide.
The resulting approximate defocus map can be used to synthesize images with shallow depth-of-field with quality
comparable to the original approach. This is demonstrated by experimental results.