Scalable Parallel Optimization of Digital Signal Processing in the Fourier Domain

The aim of the research presented in this thesis is to study different approaches to the parallel optimization of digital signal processing algorithms and optical coherence tomography methods. The parallel approaches are based on multithreading for multi-core and many-core architectures. The thesis follows the process of designing and implementing the parallel algorithms and programs and their integration into optical coherence tomography systems. Evaluations of the performance and the scalability of the proposed parallel solutions are presented. The digital signal processing considered in this thesis is divided into two groups. The first one includes generally employed algorithms operating with digital signals in Fourier domain. Those include forward and inverse Fourier transform, cross-correlation, convolution and others. The second group involves optical coherence tomography methods, which incorporate the aforementioned algorithms. These methods are used to generate cross-sectional, en-face and confocal images. Identifying the optimal parallel approaches to these methods allows improvements in the generated imagery in terms of performance and content. The proposed parallel accelerations lead to the generation of comprehensive imagery in real-time. Providing detailed visual information in real-time improves the utilization of the optical coherence tomography systems, especially in areas such as ophthalmology.