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1. The SINR Meta Distribution in Poisson Cellular Networks

   https://doi.org/10.1109/LWC.2021.3068321  

   Feng, Ke; Haenggi, Martin (April 2021, IEEE Wireless Communications Letters)

   null (Ed.)

   This work studies the signal-to-interference-plus-noise ratio (SINR) meta distribution (MD) in cellular networks with a focus on the Poisson model. Firstly, we show that for stationary base station point processes, arbitrary fading, and power-law path loss with exponent α, the base station density λ and the noise power σ2 impact the SINR MD only through ηλα/2/σ2, termed the network signal-to-noise ratio (NSNR). Next, we show that for Poisson cellular networks, the SINR MD can be written as g(x)θ-2/α when the target SINR θ and the target reliability x jointly satisfy a constraint. We derive this constraint and the integral of g(x). Lastly, we discuss several extensions of the results to more general models and architectures. 

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2. Convolutional Neural Network Denoising of Focused Ion Beam Micrographs

   https://doi.org/10.1109/MLSP52302.2021.9596272  

   Peng, Minxu; Cokbas, Mertcan; Gallastegi, Unay Dorken; Ishwar, Prakash; Konrad, Janusz; Kulis, Brian; Goyal, Vivek K (October 2021, IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP))

   Most research on deep learning algorithms for image denoising has focused on signal-independent additive noise. Focused ion beam (FIB) microscopy with direct secondary electron detection has an unusual Neyman Type A (compound Poisson) measurement model, and sample damage poses fundamental challenges in obtaining training data. Model-based estimation is difficult and ineffective because of the nonconvexity of the negative log likelihood. In this paper, we develop deep learning-based denoising methods for FIB micrographs using synthetic training data generated from natural images. To the best of our knowledge, this is the first attempt in the literature to solve this problem with deep learning. Our results show that the proposed methods slightly outperform a total variation-regularized model-based method that requires time-resolved measurements that are not conventionally available. Improvements over methods using conventional measurements and less accurate noise modeling are dramatic - around 10 dB in peak signal-to-noise ratio. 

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3. Noise from undetected sources in Dark Energy Survey images

   https://doi.org/10.1093/mnras/staa2133  

   Eckert, K; Bernstein, G M; Amara, A; Amon, A; Choi, A; Everett, S; Gruen, D; Gruendl, R A; Huff, E M; Kuropatkin, N; et al (September 2020, Monthly Notices of the Royal Astronomical Society)

   null (Ed.)

   ABSTRACT For ground-based optical imaging with current CCD technology, the Poisson fluctuations in source and sky background photon arrivals dominate the noise budget and are readily estimated. Another component of noise, however, is the signal from the undetected population of stars and galaxies. Using injection of artifical galaxies into images, we demonstrate that the measured variance of galaxy moments (used for weak gravitational lensing measurements) in Dark Energy Survey (DES) images is significantly in excess of the Poisson predictions, by up to 30 per cent, and that the background sky levels are overestimated by current software. By cross-correlating distinct images of ‘empty’ sky regions, we establish that there is a significant image noise contribution from undetected static sources (US), which, on average, are mildly resolved at DES resolution. Treating these US as a stationary noise source, we compute a correction to the moment covariance matrix expected from Poisson noise. The corrected covariance matrix matches the moment variances measured on the injected DES images to within 5 per cent. Thus, we have an empirical method to statistically account for US in weak lensing measurements, rather than requiring extremely deep sky simulations. We also find that local sky determinations can remove most of the bias in flux measurements, at a small penalty in additional, but quantifiable, noise. 

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4. Sparse Overdispersed Photon-Limited Signal Recovery with Upper and Lower Bounds

   https://doi.org/10.1109/CAMSAP58249.2023.10403449  

   Lu, Yu; Marcia, Roummel F. (December 2023, 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP))

   This study addresses the challenge of reconstructing sparse signals, a frequent occurrence in the context of overdispersed photon-limited imaging. While the noise behavior in such imaging settings is typically modeled using a Poisson distribution, the negative binomial distribution is more suitable in overdispersed scenarios where the noise variance exceeds the signal mean. Knowledge of the maximum and minimum signal intensity can be effectively utilized within the computational framework to enhance the accuracy of signal reconstruction. In this paper, we use a gradient-based method for sparse signal recovery that leverages a negative binomial distribution for noise modeling, enforces bound constraints to adhere to upper and lower signal intensity thresholds, and employs a sparsity-promoting regularization term. The numerical experiments we present demonstrate that the incorporation of these features significantly improves the reconstruction of sparse signals from overdispersed measurements. 

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5. Modeling the e-APD SAPHIRA/C-RED ONE camera at low flux level: An attempt to count photons in the near-infrared with the MIRC-X interferometric combiner

   https://doi.org/10.1051/0004-6361/201935043  

   Lanthermann, C.; Anugu, N.; Le Bouquin, J.-B.; Monnier, J. D.; Kraus, S.; Perraut, K. (May 2019, Astronomy & Astrophysics)

   Context . We implement an electron avalanche photodiode (e-APD) in the MIRC-X instrument, which is an upgrade of the six-telescope near-infrared imager MIRC, at the CHARA array. This technology should improve the sensitivity of near-infrared interferometry. Aims . We aim to characterize a near-infrared C-RED ONE camera from First Light Imaging (FLI) using an e-APD from Leonardo (previously SELEX). Methods . We first used the classical mean-variance analysis to measure the system gain and the amplification gain. We then developed a physical model of the statistical distribution of the camera output signal. This model is based on multiple convolutions of the Poisson statistic, the intrinsic avalanche gain distribution, and the observed distribution of the background signal. At low flux level, this model independently constrains the incident illumination level, the total gain, and the excess noise factor of the amplification. Results . We measure a total transmission of 48 ± 3% including the cold filter and the Quantum Efficiency. We measure a system gain of 0.49 ADU/e, a readout noise of 10 ADU, and amplification gains as high as 200. These results are consistent between the two methods and therefore validate our modeling approach. The measured excess noise factor based on the modeling is 1.47 ± 0.03, with no obvious dependency with flux level or amplification gain. Conclusions . The presented model allows the characteristics of the e-APD array to be measured at low flux level independently of a preexisting calibration. With < 0.3 electron equivalent readout noise at kilohertz frame rates, we confirm the revolutionary performances of the camera with respect to the PICNIC or HAWAII technologies. However, the measured excess noise factor is significantly higher than what is claimed in the literature (< 1.25), and explains why counting multiple photons remains challenging with this camera. 

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