CIF: Small: Signal Processing for Quanta Image Sensors: Reconstruction, Sampling, and Applications

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The proliferation of digital cameras over the past decades has revolutionized almost every aspect of science, technology and society. However, as we continue to shrink image sensors to increase spatial resolution and reduce camera size, the size of the photon-collecting site of a sensor will soon reach a fundamental limit below which no meaningful signal can be generated. The goal of this research is to investigate a new type of image sensor called the Quanta Image Sensors (QIS). Compared to conventional image sensors, QIS offer substantially higher spatial resolution, temporal resolution, and dynamic range. Because of these important features, the sensor has the potential to impact a wide range of applications in digital cameras, consumer electronics, wearable devices, medical imaging, and physical science. The focus of the research is to develop signal processing theories and algorithms to support QIS. Building upon previous studies in QIS, this research puts extra emphasis on bridging the theories to practice. Specific questions addressed include: (1) Image reconstruction. How to reconstruct images from the massive binary bit-stream collected by the QIS without using iterative algorithms? (2) Sampling. How to control the sensor?s sampling mechanism in spatial-temporal resolution, color space, and threshold to maximize the signal to noise ratio? (3) Application. How to track fast moving objects using QIS without reconstructing images? Results from this research will foster new signal processing techniques for imaging in photon limited environments.