C3 Lensless: Computation and Communication enabled on-Chip Lenseless Imager
Abstract
Cameras are becoming ubiquitous and have found innumerable DOD-relevant applications. While traditional lens-based imaging devices have served many of these applications faithfully, several emerging DOD applications such as surveillance using micro-drones and disaster recovery require image sensors to be compact, light-weight, power-efficient, and inexpensive. Existing lens-basedcameras remain constrained in terms of their size, weight, and cost. The primary reason for these constraints is the lens -- which typically accounts for more than 90% of the size, weight and cost of these cameras. Our group at Rice University has pioneered the design and development of FlatCam -- a lensless imaging system that makes order of magnitude reductions to the size, weight and cost of imaging by completely eliminating the need for a lens. In addition, many of theseemerging applications also require on-chip intelligence and communication capabilities -- so that they are able to detect and identify targets and make on-field decisions.The power requirements of state-of-art image sensing and associated machine learning algorithms make these impractical on battery-operated devices. FlatCam by combining optical computing (which is extremely power efficient), with heavily optimized on-chip machine learning algorithms, can reduce the power requirements by two orders of magnitude enabling intelligent, light-weight,compact and inexpensive imaging. With these goals in mind, we will use this DURIP award to fabricate, test and characterize C3 (Computation, and Communication enabled on-Chip) Lensless imaging devices. If realized, these advances would revolutionize imaging and situational awareness in the battlefield, providing a significant strategic benefit to our military. The equipment provided from this DURIP will be integrated with current research infrastructureto create a streamlined system to develop and characterize miniature C3 Lensless imaging devices. Combining these on-chip imaging devices with the rich mathematical tools of machine learning and optimization will fuel a new research field based on co-designing hardware and algorithms to produce novel imaging capabilities at reduced SWAPC constraints. In addition, the proposedinstruments will greatly enhance both undergraduate and graduate education/research at Rice University by exposing a new generation of students to this highly interdisciplinary research.Furthermore these chip-scale devices could have important economic impact by penetrating thecommercial markets for imaging devices in consumer imaging, medical imaging, bio-imaging, security, astronomy and other applications.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jun 13, 2019
- Source ID
- N000141912440
Entities
People
- Ashok Veeraraghavan
Organizations
- Office of Naval Research
- Rice University
- United States Navy