GPU-accelerated texture analysis using steerable Riesz wavelets

Visual pattern recognition is a key research topic in the field of image processing and computer vision with many applications including medical diagnosis, identification and classification tasks. Texture analysis based on steerable Riesz wavelets is powerful, but requires computing pixel–wise operations resulting in a run time in the order of days when large volumes of data are processed. To overcome this limitation we propose a Graphics Processing Unit (GPU) based solution. A standard CPU version is used as starting point for the development of baseline GPU versions. To further increase the performance, and to overcome compute and memory limitations we apply a series of optimization techniques, leading to five versions in total. The best performing GPU solution ensures a speed–up of 93x for the parallelized section of the application and of 29.6x for the entire application. Furthermore, we show that a higher Riesz order and/or a higher image resolution further increases the speed–up