  A shallow depth-of-field image keeps the subject in focus, and the foreground
and background contexts blurred. This effect requires much larger lens
apertures than those of smartphone cameras. Conventional methods acquire RGB-D
images and blur image regions based on their depth. However, this approach is
not suitable for reflective or transparent surfaces, or finely detailed object
silhouettes, where the depth value is inaccurate or ambiguous.
  We present a learning-based method to synthesize the defocus blur in shallow
depth-of-field images from handheld bursts acquired with a single small
aperture lens. Our deep learning model directly produces the shallow
depth-of-field image, avoiding explicit depth-based blurring. The simulated
aperture diameter equals the camera translation during burst acquisition. Our
method does not suffer from artifacts due to inaccurate or ambiguous depth
estimation, and it is well-suited to portrait photography.
