Angle Sensitive Pixels for Lensless Imaging on Spherical Sensors
This work addresses the challenge of enabling lensless imaging on curved surfaces, which could open up new application domains, though it appears incremental by building on prior planar sensor techniques.
The authors tackled the problem of lensless imaging on spherical sensors by showing that pixel orientation diversity on a curved surface improves the conditioning of the scene-to-sensor mapping, eliminating the need for modulation elements like masks. They validated this design in simulation and a lab prototype, enabling lensless imaging for curved and flexible surfaces.
We propose OrbCam, a lensless architecture for imaging with spherical sensors. Prior work in lensless imager techniques have focused largely on using planar sensors; for such designs, it is important to use a modulation element, e.g. amplitude or phase masks, to construct a invertible imaging system. In contrast, we show that the diversity of pixel orientations on a curved surface is sufficient to improve the conditioning of the mapping between the scene and the sensor. Hence, when imaging on a spherical sensor, all pixels can have the same angular response function such that the lensless imager is comprised of pixels that are identical to each other and differ only in their orientations. We provide the computational tools for the design of the angular response of the pixels in a spherical sensor that leads to well-conditioned and noise-robust measurements. We validate our design in both simulation and a lab prototype. The implications of our design is that the lensless imaging can be enabled easily for curved and flexible surfaces thereby opening up a new set of application domains.