Abstract
Photon-limited images are often seen in fields such as medical imaging. Although the number of collected photons on an image sensor statistically follows Poisson distribution, this type of noise is intractable, unlike Gaussian noise. In this study, we propose a Bayesian restoration method of Poisson corrupted image using Integrated Nested Laplace Approximation (INLA), which is a computational method to evaluate marginalized posterior distributions of latent Gaussian models (LGMs). When the original image can be regarded as ICAR (intrinsic conditional auto-regressive) model reasonably, our method performs very faster than well-known ones such as loopy belief propagation-based method and Markov chain Monte Carlo (MCMC) without decreasing the accuracy.
Abstract (translated)
光子限制图像经常出现在医学成像等领域。尽管图像传感器上收集到的光子数量在统计上遵循泊松分布，但与高斯噪声不同，这种类型的噪声是难以处理的。在本研究中，我们提出了一种利用集成嵌套拉普拉斯近似（INLA）的泊松破坏图像的贝叶斯恢复方法，这是一种评估潜在高斯模型（LGMS）边缘化后验分布的计算方法。当原始图像可以合理地看作内部条件自回归（ICAR）模型时，该方法在不降低精度的前提下，比传统的基于循环信念传播的方法和马尔可夫链蒙特卡罗（MCMC）等方法具有更快的性能。
URL
https://arxiv.org/abs/1904.01357
