论文标题
通过基于寄存器的Argmax计算实时选择性重建
Real-Time Frequency Selective Reconstruction through Register-Based Argmax Calculation
论文作者
论文摘要
频率选择性重建（FSR）是用于求解多样化图像重建任务的最新算法，其中缺少图像中像素值的子集。但是，由于其迭代，块过程，它需要高度计算复杂性，以重建缺失的像素值。尽管通过在频域中执行其计算，可以大大降低FSR的复杂性，但根据参数化，重建过程仍需要多秒钟至多分钟。但是，FSR具有大规模并行化的潜力，可以极大地改善其重建时间。在本文中，我们引入了一种新型的高度平行的FSR公式，该公式适合于现代GPU的功能，并提出了对固有的Argmax计算的相当加速的计算。总的来说，我们实现了100倍的加速，这使FSR用于实时应用程序。
Frequency Selective Reconstruction (FSR) is a state-of-the-art algorithm for solving diverse image reconstruction tasks, where a subset of pixel values in the image is missing. However, it entails a high computational complexity due to its iterative, blockwise procedure to reconstruct the missing pixel values. Although the complexity of FSR can be considerably decreased by performing its computations in the frequency domain, the reconstruction procedure still takes multiple seconds up to multiple minutes depending on the parameterization. However, FSR has the potential for a massive parallelization greatly improving its reconstruction time. In this paper, we introduce a novel highly parallelized formulation of FSR adapted to the capabilities of modern GPUs and propose a considerably accelerated calculation of the inherent argmax calculation. Altogether, we achieve a 100-fold speed-up, which enables the usage of FSR for real-time applications.
