The fields of robotics, Internet of things, health-monitoring, neuroscience, and many others have consistently trended toward miniaturization of sensing devices over the past few decades. This miniaturization has enabled new applications in areas such as connected devices, wearables, implantable medical devices, in vivo microscopy and micro-robotics. By integrating visual sensing capabilities in such devices, we can enable improved spatial, temporal and contextual information collection for artificial intelligence and data processing. However, cameras are reaching their limits in size reduction due to restrictions of traditional lensed-optics. If we can combine the design of camera optics and computational algorithms, we can potentially achieve miniaturization beyond traditional optics.

In this dissertation, we explore designing unconventional optics coupled with computational algorithms to achieve miniaturization. Recent works have shown the use of flat diffractive optics, placed at a focus distance from the imaging sensor, as a substitute for lensing and the use of computational algorithms to correct and sharpen the images. We take this a step further and place a thin diffractive mask at a very close distance (range of 100s of microns to a millimeter) from the imaging sensor, thereby achieving an even smaller form-factor. Such flat camera geometry calls for new ways of modeling the system, methods to optimize the mask design and computational algorithms to recover high-resolution images. Moreover, retaining the thin geometry, we develop a framework to design optical masks to off-load some of the computational processing to inherently zero-power optical processing. With the developed methods, we demonstrate (1) ultraminiature microscopy, (2) thickness constrained high-resolution imaging, (3) optical Gabor feature extraction, and an example of (4) hybrid optical-electronic computer vision system.