Abstract
In this paper, we investigate the implementation of image filtering in frequency domain using NVIDIA's CUDA (Compute Unified Device Architecture). In contrast to signal and image filtering in spatial domain which uses convolution operations and hence is more compute-intensive for filters having larger spatial extent, the frequency domain filtering uses FFT (Fast Fourier Transform) which is much faster and significantly reduces the computational complexity of the filtering. We implement the frequency domain filtering on CPU and GPU respectively and analyze the speed-up obtained from the CUDA's parallel processing paradigm. In order to demonstrate the efficiency of frequency domain filtering on CUDA, we implement three frequency domain filters, i.e., Butter worth, low-pass and Gaussian for processing different sizes of images on CPU and GPU respectively and perform the GPU vs. CPU benchmarks. The results presented in this paper show that the frequency domain filtering with CUDA achieves significant speed-up over the CPU processing in frequency domain with the same level of (output) image quality on both the processing architectures.