Abstract
A quanta image sensor (QIS) is a class of single-photon imaging devices that measure light intensity using oversampled binary observations. Because of the stochastic nature of the photon arrivals, data acquired by QIS is a massive stream of random binary bits. The goal of image reconstruction is to recover the underlying image from these bits. In this paper, we present a non-iterative image reconstruction algorithm for QIS. Unlike existing reconstruction methods that formulate the problem from an optimization perspective, the new algorithm directly recovers the images through a pair of nonlinear transformations and an off-the-shelf image denoising algorithm. By skipping the usual optimization procedure, we achieve orders of magnitude improvement in speed and even better image reconstruction quality. We validate the new algorithm on synthetic datasets, as well as real videos collected by one-bit single-photon avalanche diode (SPAD) cameras.
Highlights
IntroductionSince the birth of charge coupled devices (CCD) in the late 1960s [1] and the complementary metal-oxide-semiconductor (CMOS) active pixel sensors in the early 1990s [2], the pixel pitch of digital image sensors has been continuously shrinking [3]
Solution, i.e., a summation followed by the inverse incomplete Gamma transform, and an alternating direction method of multipliers (ADMM) algorithm using a total variation regularization [29,31]
Apart form the peak signal to noise ratio (PSNR) values, we report the runtimes of the algorithms
Summary
Since the birth of charge coupled devices (CCD) in the late 1960s [1] and the complementary metal-oxide-semiconductor (CMOS) active pixel sensors in the early 1990s [2], the pixel pitch of digital image sensors has been continuously shrinking [3]. As pixel pitch shrinks, the amount of photon flux detectable by each pixel drops, leading to reduced signal strength. The maximum number of photoelectrons that can be held in each pixel, known as the full-well capacity, drops. Small full-well capacity causes reduced maximum signal-to-noise ratio and lowers the dynamic range of an image [4]. Pushing for smaller pixels, feasible in the near future, will become a major technological hurdle to new image sensors
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