5 accepted papers
Equivariant imaging (EI) enables training signal reconstruction models without requiring ground truth data by leveraging signal symmetries. Deep equilibrium models (DEQs) are a powerful class of neural networks where the output is a fixed point of a learned operator. However, training DEQs with comp…
Single-photon lidar (SPL) can achieve high-accuracy, lowlight ranging; however, velocity estimation typically requires regression over multiple distance measurements. Here, we introduce Doppler SPL, which enables joint instantaneous velocity and range estimation. First, we derive a measurement model…
Conventional wisdom suggests that single-photon lidar (SPL) should operate in low-light conditions to minimize dead-time effects.Many methods have been developed to mitigate these effects in synchronous SPL systems. However, solutions for free-running SPL remain limited despite the advantage of redu…
Readout multiplexing is a promising solution to overcome hardware limitations and data bottlenecks in imaging with single-photon detectors. Conventional multiplexed readout processing creates an upper bound on photon counts at a very fine time scale, where frames with multiple detected photons must…
Convolutional neural networks (CNN) have emerged as a powerful tool for solving computational imaging reconstruction problems. However, CNNs are generally difficult-to-understand black-boxes. Accordingly, it is challenging to know when they will work and, more importantly, when they will fail. This…