Title 
Digital Sampling and Imaging from Quantization Noise

Abstract

Digital data capture is the backbone of all modern day systems and “Digital Revolution” has been aptly termed as the Third Industrial Revolution. Underpinning the digital representation is the Shannon-Nyquist sampling theorem. In this context, hardware and consumer technologies strive for high resolution quantization based acquisition.

Antithetical to folk wisdom, we consider the problem of recording quantization noise as measurements and show that this results in unconventional advantages in computational sensing and imaging, previously overlooked in the literature. To this end, we introduce the Unlimited Sensing Framework (USF) that is based on a co-design of hardware and algorithms. On the hardware front, our work is based on a radically different analog-to-digital converter (ADC) design, which allows for the ADCs to produce quantization noise. This is equivalent to recording the fractional part of the signal. On the algorithms front, we develop new, mathematically guaranteed recovery strategies. 

In the first part of this talk, we prove a sampling theorem akin to the Shannon-Nyquist criterion followed by certain variations on the theme. We show that, remarkably, despite the non-linearity in the sensing pipeline, the sampling rate only depends on the signal’s bandwidth. Our theory is complemented with a stable recovery algorithm. We then discuss sampling of sparse and parametric signals. Beyond the theoretical results, we will also present a hardware demo that shows our approach in action.

Moving further, we reinterpret the USF as a generalized linear model that motivates a new class of inverse problems. We conclude this talk by presenting a research overview in the context of single-shot high-dynamic-range (HDR) imaging, sensor array processing, HDR computed tomography based on the modulo Radon transform and massive MIMO technology. 

Speaker

Ayush Bhandari received the Ph.D. degree from Massachusetts Institute of Technology (MIT), Cambridge, MA, USA, in 2018, for his work on computational sensing and imaging which led to the co-authored book Computational Imaging (MIT Press, 2022). He is currently a faculty member with the Department of Electrical and Electronic Engineering, Imperial College London, U. K. He has held research positions at INRIA (Rennes), France, Nanyang Technological University, Singapore, the Chinese University of Hong Kong and Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland among other institutes. He was appointed the August–Wilhelm Scheer Visiting Professor (Department of Mathematics), in 2019 by the Technical University of Munich.

He has been a tutorial speaker at various venues including the ACM Siggraph (2014,2015) and the IEEE ICCV (2015) and he was the keynote speaker at the Intl. Workshop on Compressed Sensing applied to Radar, Multimodal Sensing and Imaging (CoSeRa), 2018. Some aspects of his work have led to new sensing and imaging modalities which have been widely covered in press and media (e.g. BBC news). Applied aspects of his research have led to more than 10 US patents. His scientific contributions have led to numerous prizes, most recently, the Best Paper Award at IEEE ICCP 2020 (Intl. Conf. on Computational Photography) and the Best Student Paper Award (senior co-author) at IEEE ICASSP 2019 (Intl. Conf. on Acoustics, Speech and Signal Processing). In 2020, his doctoral work was awarded the Best PhD Dissertation Award from the IEEE Signal Processing Society. In 2021, he received the President's Medal for Outstanding Early Career Researcher at Imperial College London.