2012/06/02 by Joseph Salmon, Zachary Harmany, Salmon, Joseph +7 · 12 citations
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #Computation (stat.CO) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Detector #FOS: Computer and information sciences #Heteroscedasticity #Image (mathematics) #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Machine learning #Mathematics #Noise (video) #Noise reduction #Pattern recognition (psychology) #Photoacoustic and Ultrasonic Imaging #Poisson distribution #Principal component analysis #Shot noise #Sparse and Compressive Sensing Techniques #Statistics #cs.CV #cs.LG #stat.CO
paper · pdf · doi:10.48550/arxiv.1206.0338
published in arXiv (Cornell University) (Cornell University) · erratum: Image man is wrongly name pepper in the journal version
openalex publication_date 2012/06/02 · arxiv created 2014/04/28 · arxiv updated 2014/04/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.