Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications. Existing scatter correction and density reconstruction methods may not provide the high accuracy needed in many applications and can break down in the presence of unmodeled or anomalous scatter and other experimental artifacts. Incorporating machine-learned models could prove beneficial for accurate density reconstruction particularly in dynamic imaging, where the time-evolution of the density fields could be captured by partial differential equations or by learning from hydrodynamics simulations. In this work, we demonstrate the ability of learned deep neural networks to perform artifact removal in noisy density reconstructions, where the noise is imperfectly characterized. We use a Wasserstein generative adversarial network (WGAN), where the generator serves as a denoiser that removes artifacts in densities obtained from traditional reconstruction algorithms. We train the networks from large density time-series datasets, with noise simulated according to parametric random distributions that may mimic noise in experiments. The WGAN is trained with noisy density frames as generator inputs, to match the generator outputs to the distribution of clean densities (time-series) from simulations. A supervised loss is also included in the training, which leads to improved density restoration performance. In addition, we employ physics-based constraints such as mass conservation during network training and application to further enable highly accurate density reconstructions. Our preliminary numerical results show that the models trained in our frameworks can remove significant portions of unknown noise in density time-series data.
翻译:现有散射校正和密度重建方法可能无法提供许多应用中所需的高精度,而且可以在存在未经改造或异常散射和其他实验文物的情况下分解。纳入机器学习模型可能证明有利于准确的密度重建,特别是在动态成像中,密度域的时间变化可以通过部分差异方程或从流体动力模拟中学习来采集。在这项工作中,我们展示了学习的深层神经网络有能力在噪音密集度重建中进行艺术品清除,噪音特征不完全。我们使用了瓦斯特斯坦基因对抗网络(WGAN),在该网络中,发电机作为拆卸从传统重建算法中获得的密度的文物的脱弦器。我们用大量密度时间序列数据集来对网络进行培训,根据实验中可能模拟噪音的参数随机分布进行模拟。我们用噪声密度框架作为发电机投入进行训练,使发电机产出与清洁密度模型(时间序列)的分布相匹配。我们使用瓦斯特斯坦基因基因变异性对抗性对抗性对抗网络,在模拟中进行大量数据重建,我们经过监督的损耗损率调整后,我们又将进行不甚深的深度再进行数据重组。