Compressive Epsilon Photography for Post-Capture Control in Digital Imaging

A traditional camera requires the photographer to select the many parameters at capture time. While advances in light field photography have enabled post-capture control of focus and perspective, they suffer from several limitations including lower spatial resolution, need for hardware modifications, and restrictive choice of aperture and focus setting. In this paper, we propose "compressive epsilon photography," a technique for achieving complete post-capture control of focus and aperture in a traditional camera by acquiring a carefully selected set of 8 to 16 images and computationally reconstructing images corresponding to all other focus-aperture settings. We make the following contributions: first, we learn the statistical redundancies in focal-aperture stacks using a Gaussian Mixture Model; second, we derive a greedy sampling strategy for selecting the best focus-aperture settings; and third, we develop an algorithm for reconstructing the entire focal-aperture stack from a few captured images. As a consequence, only a burst of images with carefully selected camera settings are acquired. Post-capture, the user can then select any focal-aperture setting of choice and the corresponding image can be rendered using our algorithm. We show extensive results on several real data sets.