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.
@article{arxiv.2202.13926,
title = {Real-Time Frequency Selective Reconstruction through Register-Based Argmax Calculation},
author = {Andy Regensky and Simon Grosche and Jürgen Seiler and André Kaup},
journal= {arXiv preprint arXiv:2202.13926},
year = {2022}
}
Comments
6 pages, 5 figures, Source code available at https://gitlab.lms.tf.fau.de/LMS/gpu-fsr