Single photon counting avalanche diodes (SPADs) are versatile sensors for active and time-correlated measurements such as ranging and fluorescence imaging. These detectors also have great potential for passive or uncorrelated imaging. Recently, it was demonstrated that passive imaging of photon flux is possible by determining the mean photon arrival time. For ambient light illumination, timestamp data can be interpreted as a metric for the photon impingement rate. Various applications have been investigated including high-dynamic-range imaging, single-photon imaging, and capture of fast-moving objects or dynamic scenes. However, the appearance of noise and motion blur requires sophisticated signal processing that enables sub-pixel resolution imaging and reconstruction of the scene by motion compensation. In this paper, we present new results on the evaluation of global scene motion. In our approach, motion is intentionally generated by a rotating wedge prism, resulting in continuous global motion on a circular path. We have studied scenes with different optical contrast.
KEYWORDS: Super resolution, Denoising, Sensors, Cameras, Reconstruction algorithms, Signal to noise ratio, Fluctuations and noise, Photodetectors, Video, Single photon
Single-photon sensitive image sensors have recently gained popularity in passive imaging applications where the goal is to capture photon flux (brightness) values of different scene points in the presence of challenging lighting conditions and scene motion. Recent work has shown that high-speed bursts of single-photon timestamp information captured using a single-photon avalanche diode camera can be used to estimate and correct for scene motion thereby improving signal-to-noise ratio and reducing motion blur artifacts. We perform a comparison of various design choices in the processing pipeline used for noise reduction, motion compensation, and upsampling of single-photon timestamp frames. We consider various pixelwise noise reduction techniques in combination with state-of-the-art deep neural network upscaling algorithms to super-resolve intensity images formed with single-photon timestamp data. We explore the trade space of motion blur and signal noise in various scenes with different motion content. Using real data captured with a hardware prototype, we achieved super-resolution reconstruction at frame rates up to 65.8 kHz (native sampling rate of the sensor) and captured videos of fast-moving objects. The best reconstruction is obtained with the motion compensation approach, which achieves a structural similarity (SSIM) of about 0.67 for fast-moving rigid objects. We are able to reconstruct subpixel resolution. These results show the relative superiority of our motion compensation compared to other approaches that do not exceed an SSIM of 0.5.
KEYWORDS: Single photon, Sensors, Super resolution, Nonlinear filtering, Cameras, Digital filtering, Image quality, Linear filtering, Fluctuations and noise, Electronic filtering
Single photon-counting avalanche photo-diode (SPAD) can measure the photon flux from uncorrelated single photons. In present work, we show how the sensor photon count rate is related to the intensity or the radiant flux that is reflected from surfaces in the sensor's field of view and incident on the sensor array. After a brief theoretical discussion of photon flux imaging, we examine various de-noising strategies and the effect of motion blur. Finally, we present the application of a fast super-resolution neural network (FSRCNN) to scale image by a scaling factor of 3× to obtain super-resolution images (32 × 32 → 96 × 96).
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