Defocus Magnification Using Conditional Adversarial Networks

摘要

Defocus magnification is the process of rendering a shallow depth-of-field in an image captured using a camera with a narrow aperture. Defocus magnification is a useful tool in photography for emphasis on the subject and for highlighting background bokeh. Estimating the per-pixel blur kernel or the depth-map of the scene followed by spatially-varying re-blurring is the standard approach to defocus magnification. We propose a single-step approach that directly converts a narrow-aperture image to a wide-aperture image. We use a conditional adversarial network trained on multi-aperture images created from light-fields. We use a novel loss term based on a composite focus measure to improve generalization and show high quality defocus magnification.