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High Speed Imaging from Sparse Photon Counts

Arxiv.org paper "A `Little Bit' Too Much? High Speed Imaging from Sparse Photon Counts" by Paramanand Chandramouli, Samuel Burri, Claudio Bruschini, Edoardo Charbon, and Andreas Kolb from University of Siegen, Germany, and Swiss Federal Institute of Technology, Lausanne, Switzerland shows the power of machine learning in recovering nice images from single-photon mess:

"Recent advances in photographic sensing technologies have made it possible to achieve light detection in terms of a single photon. Photon counting sensors are being increasingly used in many diverse applications. We address the problem of jointly recovering spatial and temporal scene radiance from very few photon counts. Our ConvNet-based scheme effectively combines spatial and temporal information present in measurements to reduce noise. We demonstrate that using our method one can acquire videos at a high frame rate and still achieve good quality signal-to-noise ratio. Experiments show that the proposed scheme performs quite well in different challenging scenarios while the existing denoising schemes are unable to handle them."

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