Improving Model Adaptation For Semantic Segmentation By Learning Model-invariant Features With Multiple Source-Domain Models

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.