  Image Processing is a specialized area of Digital Signal Processing which
contains various mathematical and algebraic operations such as matrix
inversion, transpose of matrix, derivative, convolution, Fourier Transform etc.
Operations like those require higher computational capabilities than daily
usage purposes of computers. At that point, with increased image sizes and more
complex operations, CPUs may be unsatisfactory since they use Serial Processing
by default. GPUs are the solution that come up with greater speed compared to
CPUs because of their Parallel Processing/Computation nature. A parallel
computing platform and programming model named CUDA was created by NVIDIA and
implemented by the graphics processing units (GPUs) which were produced by
them. In this paper, computing performance of some commonly used Image
Processing operations will be compared on OpenCV's built in CPU and GPU
functions that use CUDA.

Distributed, Parallel, and Cluster Computing