Poisson noise reduction with non-local PCA

Photon-limited imaging arises when the number of photons collected by a sensor array is small relative to the number of detector elements. Photon limitations are an important concern for many applications such as spectral imaging, night vision, nuclear medicine, and astronomy. Typically a Poisson distribution is used to model these observations, and the inherent heteroscedasticity of the data combined with standard noise removal methods yields significant artifacts. This paper introduces a novel denoising algorithm for photon-limited images which combines elements of dictionary learning and sparse patch-based representations of images. The method employs both an adaptation of Principal Component Analysis (PCA) for Poisson noise and recently developed sparsity-regularized convex optimization algorithms for photon-limited images. A comprehensive empirical evaluation of the proposed method helps characterize the performance of this approach relative to other state-of-the-art denoising methods. The results reveal that, despite its conceptual simplicity, Poisson PCA-based denoising appears to be highly competitive in very low light regimes.Comment: erratum: Image man is wrongly name pepper in the journal versio

Sparsity Based Poisson Denoising with Dictionary Learning

The problem of Poisson denoising appears in various imaging applications, such as low-light photography, medical imaging and microscopy. In cases of high SNR, several transformations exist so as to convert the Poisson noise into an additive i.i.d. Gaussian noise, for which many effective algorithms are available. However, in a low SNR regime, these transformations are significantly less accurate, and a strategy that relies directly on the true noise statistics is required. A recent work by Salmon et al. took this route, proposing a patch-based exponential image representation model based on GMM (Gaussian mixture model), leading to state-of-the-art results. In this paper, we propose to harness sparse-representation modeling to the image patches, adopting the same exponential idea. Our scheme uses a greedy pursuit with boot-strapping based stopping condition and dictionary learning within the denoising process. The reconstruction performance of the proposed scheme is competitive with leading methods in high SNR, and achieving state-of-the-art results in cases of low SNR.Comment: 13 pages, 9 figure

The X-ray Power Spectral Density Function and Black Hole Mass Estimate for the Seyfert AGN IC 4329a

We present the X-ray broadband power spectral density function (PSD) of the X-ray-luminous Seyfert IC 4329a, constructed from light curves obtained via Rossi X-ray Timing Explorer monitoring and an XMM-Newton observation. Modeling the 3-10 keV PSD using a broken power-law PSD shape, a break in power-law slope is significantly detected at a temporal frequency of 2.5(+2.5,-1.7) * 10^-6 Hz, which corresponds to a PSD break time scale T_b of 4.6(+10.1,-2.3) days. Using the relation between T_b, black hole mass M_BH, and bolometric luminosity as quantified by McHardy and coworkers, we infer a black hole mass estimate of M_BH = 1.3(+1.0,-0.3) * 10^8 solar masses and an accretion rate relative to Eddington of 0.21(+0.06,-0.10) for this source. Our estimate of M_BH is consistent with other estimates, including that derived by the relation between M_BH and stellar velocity dispersion. We also present PSDs for the 10-20 and 20-40 keV bands; they lack sufficient temporal frequency coverage to reveal a significant break, but are consistent with the same PSD shape and break frequency as in the 3-10 keV band.Comment: Accepted for publication in ApJ. 11 pages, 6 figures (5 color