![]() ![]() 19 – 22 In this approach, the time between photon events is determined from the mean event time. Furthermore, the timing ability of SPAD sensors was used to determine the physical intensity by estimation of the photon flux from the photon impingement rate. Recent publications focused on the passive sensing capabilities of single photon counting devices by, for instance, restoring intensity images from binary photon detection 23 – 28 for both static and dynamic scenes. Although these passively acquired photon timestamps do not provide information about the 3D scene structure, by exploiting the Poisson timing statistics, it was recently shown 19 – 22 that these passive timestamps provide scene intensity information. These timestamps are not correlated with any active light source and are instead recorded with respect to the SPAD camera’s frame start times. We consider the problem of passive imaging with a SPAD camera where each SPAD pixel records photon timestamps due to ambient light naturally present in the scene. Each pixel thus observes the photon impingement time which is correlated to the scene response such as in fluorescence lifetime microscopy, 7 range imaging LiDAR, 8 – 11 super-resolution ranging, 12 transient, 13, 14 and nonline-of-sight sensing. Typically, SPAD sensors are used in conjunction with an active light source (e.g., a pulsed laser) to record the photon timestamps in synchronization with the pulsed illumination source. These abilities are due to the detection of single-photon impacts triggering avalanche effects and time tagging with a resolution of a few picoseconds. SPAD sensors have an outstanding sensitivity, low dark count rates (DCR), and high time resolution. Pixel fill-factors can be improved through the use of three-dimensional (3D)-stacking and microlens arrays. Often, sensors and readout circuits are fabricated side-by-side on the same chip, representing a high degree of integration and resulting in short signal propagation times. SPAD sensors can be integrated and manufactured inexpensively in standardized semiconductor manufacturing processes with a wide range of pixel array sizes, from single pixel detectors to megapixel SPAD arrays. In recent years, single photon-counting avalanche diode (SPAD) sensors have gained popularity for use in various optronic sensing applications due to their extreme sensitivity and the ability to precisely measure the time of arrival of individual photons. These results show the relative superiority of our motion compensation compared to other approaches that do not exceed an SSIM of 0.5. We are able to reconstruct subpixel resolution. ![]() 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. 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. We explore the trade space of motion blur and signal noise in various scenes with different motion content. ![]() ![]() 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 perform a comparison of various design choices in the processing pipeline used for noise reduction, motion compensation, and upsampling of single-photon timestamp frames. 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. 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. ![]()
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