Imaging and Computing Seminar

Yue Lu , SEAS, Harvard

Gigapixel Binary Sensing: Image Acquisition Using One-Bit Poisson Statistics

Before the advent of digital image sensors, photography, for the most part of its history, used film to record light information. In this talk, I will present a new digital image sensor that is reminiscent of photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity.

To analyze its performance, we formulate the binary sensing scheme as a parameter estimation problem based on quantized Poisson statistics. We show that, with a single-photon quantization threshold and large oversampling factors, the Cramer-Rao lower bound of the estimation variance approaches that of an ideal unquantized sensor, that is, as if there were no quantization in the sensor measurements. Furthermore, this theoretical performance bound is shown to be asymptotically achievable by practical image reconstruction algorithms based on maximum likelihood estimators.

Numerical results on both synthetic data and images taken by a prototype sensor verify the theoretical analysis and the effectiveness of the proposed image reconstruction algorithm. They also demonstrate the benefit of using the new binary sensor in applications involving high dynamic range imaging.

Joint work with Feng Yang, Luciano Sbaiz and Martin Vetterli.