Directional Decomposition of Images: Implementation Issues Including GPU Techniques

Abstract

Directional decomposition of an image consists of separating it into several components, each containing directional information in some specific directions. It has many applications in digital image processing, such as image improvement or linear feature detection, and could be used on seismic data to help geophysicists finding faults. In this thesis, we look at a directional filter bank (DFB) introduced by Bamberger and Smith and how to implement it efficiently on CPU and GPU. Graphics Processing Units (GPUs) are becoming increasingly more suitable for general scientific computing, and applications with suitable properties run much quicker on a GPU than a CPU. For instance, NVIDIA CUDA (Compute Unified Device Architecture) is a new programming interface that lets users program NVIDIA General Purpose GPUs (GPGPUs) in a C-like fashion for data parallel intensive computation. We translate the DFB algorithm from a theoretical signal processing description to an algorithmic description from computer scientists'point of view, including a readable C implementation. Tools are developed to ease our DFB investigation, including a tailored library to manipulate images in suitable text-based and binary formats and for generating test images with suitable properties. Several implementations of 1D filter banks are also provided. Finally, part of the Bamberger DFB is implemented efficiently using the CUDA environment for NVIDIA GPUs. We show that directional filter banks can efficiently be executed on GPUs and demonstrate that the CPU-GPU bandwidth affects performance considerably. Hence, care should be taken to do as many steps as possible on the GPU before returning results to the CPU.