Wednesday, November 07, 2018

High Speed Imaging from Sparse Photon Counts 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."


  1. Very nice indeed, one can envisage a scheme whereby a 2D single-photon 1-Bit image/video stream is augmented by this spatial and frame-rate temporal CNN but then with the added benefit of the data from photon time of arrival TDCs, or even a multi-spectral approach.

  2. Where can this paper be downloaded please ? Thanks !

  3. Well, on the QIS side we have been doing this for a couple years now. The paper misrepresents past work in the "Quanta Imaging" paragraph:
    They say "The main difference between these reconstruction methods and
    our scheme is that these algorithms require spatial oversampling
    and cannot handle temporal variations." This statement is quite untrue.

    Aside from that, this shows what is coming in either CIS or SPAD based QIS. It is pretty amazing.

    See also the special issue on Photon-Counting Image Sensors


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