Efficient Reconstruction of Low Photon Count Images from a High Speed Camera

Abstract

Challenging imaging applications requiring ultra-short exposure times or imaging in photon-starved environments can acquire extremely low numbers of photons per pixel, (<1 photon per pixel). Such photon-sparse images can require post-processing techniques to improve the retrieved image quality as defined quantitatively by metrics including the Structural Similarity Index Measure (SSIM) and Mean Squared Error (MSE) with respect to the ground truth. Bayesian retrodiction methods have been shown to improve estimation of the number of photons detected and spatial distributions in single-photon imaging applications. In this work, we demonstrate that at high frame rates (>1 MHz) and low incident photon flux (<1 photon per pixel), image post processing can provide better grayscale information and spatial fidelity of reconstructed images than simple frame averaging, with improvements in SSIM up to a factor of 3. Various other image post-processing techniques are also explored and some of which result in a similar quality of image reconstruction to Bayesian retrodiction, with lower computational load. Image reconstructions using Bayesian Retrodiction or bilateral filtering are of comparable quality to frame averaging, as measured by the Structural Similarity Index Measure, when using less than 40% of the photon flux.