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

Direct Handheld Burst Imaging to Simulated Defocus