Computed tomography is widely used to examine internal structures in a non-destructive manner. To obtain high-quality reconstructions, one typically has to acquire a densely sampled trajectory to avoid angular undersampling. However, many scenarios require a sparse-view measurement leading to streak-artifacts if unaccounted for. Current methods do not make full use of the domain-specific information, and hence fail to provide reliable reconstructions for highly undersampled data. We present a novel framework for sparse-view tomography by decoupling the reconstruction into two steps: First, we overcome its ill-posedness using a super-resolution network, SIN, trained on the sparse projections. The intermediate result allows for a closed-form tomographic reconstruction with preserved details and highly reduced streak-artifacts. Second, a refinement network, PRN, trained on the reconstructions reduces any remaining artifacts. We further propose a light-weight variant of the perceptual-loss that enhances domain-specific information, boosting restoration accuracy. Our experiments demonstrate an improvement over current solutions by 4 dB.

翻译：为了进行高质量的重建,人们通常必须获得密集的取样轨迹,以避免进行角形下取样;然而,许多假设情况需要一种稀疏的测量,如果下落不明,则会导致线状的艺术行为。目前的方法没有充分利用特定领域的信息,因此无法为高度低温的数据提供可靠的重建。我们提出了一个稀薄的视觉断层图像的新框架,将重建分为两个步骤:第一,我们利用一个超分辨率网络SIN(SIN)(通过对稀释预测进行的培训)克服其不正确性。中间结果使得能够用保存的细节进行封闭式的图象重建,并大量减少线性艺术行为。第二,一个精细网络(PRN)(通过在重建方面受过培训)减少了任何剩余的文物。我们进一步提议一个轻量的视觉损失变式,以加强特定领域的信息,提高恢复的准确性。我们的实验表明,4 dB(dB)比目前的解决办法有所改进。