Lensless cameras, while extremely useful for imaging in constrained scenarios, struggle with resolving scenes with large depth variations. To resolve this, we propose imaging with a set of mask patterns displayed on a programmable mask, and introduce a computational focusing operator that helps to resolve the depth of scene points. As a result, the proposed imager can resolve dense scenes with large depth variations, allowing for more practical applications of lensless cameras. We also present a fast reconstruction algorithm for scene at multiple depths that reduces reconstruction time by two orders of magnitude. Finally, we build a prototype to show the proposed method improves both image quality and depth resolution of lensless cameras.

Lensless cameras provide a framework to build thin imaging systems by replacing the lens in a conventional camera with an amplitude or phase mask near the sensor. Existing methods for lensless imaging can recover the depth and intensity of the scene, but they require solving computationally-expensive inverse problems. Furthermore, existing methods struggle to recover dense scenes with large depth variations. In this paper, we propose a lensless imaging system that captures a small number of measurements using different patterns on a programmable mask. In this context, we make three contributions. First, we present a fast recovery algorithm to recover textures on a fixed number of depth planes in the scene. Second, we consider the mask design problem, for programmable lensless cameras, and provide a design template for optimizing the mask patterns with the goal of improving depth estimation. Third, we use a refinement network as a post-processing step to identify and remove artifacts in the reconstruction. These modifications are evaluated extensively with experimental results on a lensless camera prototype to showcase the performance benefits of the optimized masks and recovery algorithms over the state of the art.