What Does a One-Bit Quanta Image Sensor Offer?

Abstract

The one-bit quanta image sensor (QIS) is a photoncounting device that captures image intensities using binary bits. Assuming that the analog voltage generated at the floating diffusion of the photodiode follows a Poisson-Gaussian distribution, the sensor produces either a "1" if the voltage is above a certain threshold or "0" if it is below the threshold. The concept of this binary sensor has been proposed for more than a decade and physical devices have been built to realize the concept. However, what benefits does a one-bit QIS offer compared to a conventional multi-bit CMOS image sensor? Besides the known empirical results, are there theoretical proofs to support these findings?The goal of this paper is to provide new theoretical support from a signal processing perspective. In particular, it is theoretically found that the sensor can offer three benefits: (1) Low-light: One-bit QIS performs better at low-light because it has a low read noise and its one-bit quantization can produce an error-free measurement. However, this requires the exposure time to be appropriately configured. (2) Frame rate: One-bit sensors can operate at a much higher speed because a response is generated as soon as a photon is detected. However, in the presence of read noise, there exists an optimal frame rate beyond which the performance will degrade. A Closed-form expression of the optimal frame rate is derived. (3) Dynamic range: Onebit QIS offers a higher dynamic range. The benefit is brought by two complementary characteristics of the sensor: nonlinearity and exposure bracketing. The decoupling of the two factors is theoretically proved, and closed-form expressions are derived.

eRPCA: Robust Principal Component Analysis for Exponential Family Distributions

“…QIS is a photon‐counting device that captures image intensities using binary bits [11], which can be modeled via i.i.d. Bernoulli noise [9]. For (ii), anomalies arise in the form of defects in the steel manufacturing process, for example, gashes, dents, or inhomogeneities on the steel sheet.…”

“…Similar formulations can be adopted for other exponential family distributions. This canonical decomposition again retains a convex formulation in the natural parameter 𝜂 and does not require additional constraints on H when solving (9), since the range of the canonical parameter 𝜂 is over the reals [8]. In problems where there is domain knowledge on where low-rank structure is expected to arise, such information should be used first and foremost for guiding the formulation of this low-rank plus sparse decomposition.…”

“…Defects are typically detected via careful monitoring of images of the steel sheets taken from high-frequency cameras. Recent studies, for example, Chan [9], have shown that QIS ( [11]) may offer improved higher frequency imaging with lower read noise over more conventional multi-bit imaging systems (e.g., complementary metal-oxide semiconductor imaging [ [11]]). The key challenge in defect detection using QIS is that its image intensities take the form of binary bits, which can be well-modeled via i.i.d.…”

“…This application features the two defining challenges motivating our method: (i) non‐Gaussian noise with (ii) significant sparse anomalies. For (i), the high‐frequency imaging of steel sheets can be performed via QIS [11], which has shown improved performance over more conventional multi‐bit systems (e.g., complementary metal‐oxide semiconductor imaging [11]) due to higher frequency imaging with lower read noise [9]. QIS is a photon‐counting device that captures image intensities using binary bits [11], which can be modeled via i.i.d.…”

“…The key challenge in defect detection using QIS is that its image intensities take the form of binary bits, which can be well‐modeled via i.i.d. Bernoulli noise [9]; this thus presents an appropriate application for the $$$ {e}^{\mathrm{RPCA}} $$$.…”