Bayesian Restoration of High-Dimensional Photon-Starved Images
Bayesian Restoration of High-Dimensional Photon-Starved Images
复制标题
高维光子匮乏图像的贝叶斯恢复
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
J. Tourneret
中科院分区：
文献类型：
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作者：
Julián Tachella;Y. Altmann;M. Pereyra;S. Mclaughlin;J. Tourneret
本文研究了不同的算法，从单光子测量破坏泊松噪声进行图像恢复。恢复问题在贝叶斯框架中进行了阐述，并考虑了几种最先进的蒙特卡罗采样器来估计未知图像并量化其不确定性。不同的采样器进行了比较，通过一系列的实验与合成图像。结果表明，随着问题维数的增加和光子数的减少，所提出的采样器的缩放特性。此外，我们的实验表明，对于一定的光子预算（即，成像设备的采集时间），对观察进行下采样可以产生更好的重建结果。
This paper investigates different algorithms to perform image restoration from single-photon measurements corrupted with Poisson noise. The restoration problem is formulated in a Bayesian framework and several state-of-the-art Monte Carlo samplers are considered to estimate the unknown image and quantify its uncertainty. The different samplers are compared through a series of experiments conducted with synthetic images. The results demonstrate the scaling properties of the proposed samplers as the dimensionality of the problem increases and the number of photons decreases. Moreover, our experiments show that for a certain photon budget (i.e., acquisition time of the imaging device), downsampling the observations can yield better reconstruction results.