Many contemporary visualization tools comprise some image filtering approach. Since image filtering approaches are very computationally demanding, the acceleration using graphics-hardware (GPU) is very desirable to preserve interactivity of the main visualization tool itself. In this article we take a close look on GPU implementation of two basic approaches to image filtering - Fast Fourier Transform (frequency domain) and convolution (spatial domain). We evaluate these methods in terms of the performance in real time applications and suitability for GPU implementation. Convolution yields better performance than Fast Fourier Transform (FFT) in many cases; however, this observation cannot be generalized. In this article we identify conditions under which the FFT gives better performance than the corresponding convolution and we assess the different kernel sizes and issues of application of multiple filters on one image.