论文标题
使用形状先验的生成对抗网络增强辛克图
Sinogram Enhancement with Generative Adversarial Networks using Shape Priors
论文作者
论文摘要
通过从计算模型中推断出稀缺测量是一种解决逆问题不当的方法。我们通过使用生成模型完成一系列采集以及有关扫描对象的事先知识来解决有限的角度层析成像。使用生成的对抗网络作为模型和计算机辅助的设计数据作为先验，我们证明了我们技术比其他最新方法的定量和定性优势。推断出大量连续丢失的测量值，我们为其他图像介绍技术提供了一种替代方法，而这些图像介绍技术却没有为我们的研究问题提供令人满意的答案：是否可以通过使用生成模型来推断缺少测量值来减少X射线博览会？
Compensating scarce measurements by inferring them from computational models is a way to address ill-posed inverse problems. We tackle Limited Angle Tomography by completing the set of acquisitions using a generative model and prior-knowledge about the scanned object. Using a Generative Adversarial Network as model and Computer-Assisted Design data as shape prior, we demonstrate a quantitative and qualitative advantage of our technique over other state-of-the-art methods. Inferring a substantial number of consecutive missing measurements, we offer an alternative to other image inpainting techniques that fall short of providing a satisfying answer to our research question: can X-Ray exposition be reduced by using generative models to infer lacking measurements?
