We describe a new class of computational optical sensors and imagers that do not rely on traditional refractive or reflective focusing but instead on special diffractive optical elements integrated with CMOS photodiode arrays. Images are not captured, as in traditional imaging systems, but rather computed from raw photodiode signals. Because such imagers forgo the use of lenses, the sensor portions can be made unprecedentedly small—roughly as small as the cross-section of a human hair. Such imagers have extended depth of field, from roughly 1mm to infinity, and should find use in numerous applications, from endoscopy to infra-red and surveillance imaging, automotive imaging and more. Furthermore, the gratings and signal processing can be tailored to specific applications from visual motion estimation to barcode reading and others.
David G. Stork is Rambus Fellow and weads research in the Computational Sensing and Imaging Group at Rambus Labs. A graduate in physics from MIT and the University of Maryland, Dr. Stork has published eight books/proceedings volumes, including Pattern classification (2nd ed.) and Seeing the Light: Optics in nature, photography, color, vision and holography and has held faculty appointments in eight disciplines variously at Wellesley and Swarthmore Colleges and Clark, Boston and Stanford Universities. He holds 47 issued patents and is Fellow of the International Association for Pattern Recognition (IAPR), the International Academy, Research, and Industry Association (IARIA), the Society for Photographic Instrumentation and Engineering (SPIE) and the Optical Society of America (OSA) and a Senior Member of IEEE.
This seminar is sponsored by Stanford OSA