Better results can be achieved with a Gaussian shaped filter function. The advantage is that the Gaussian has the same shape in the spatial and Fourier domains and therefore does not incur the ringing effect in the spatial domain of the filtered image. A commonly used discrete approximation to the Gaussian is the Butterworth filter. Applying this filter in the frequency domain shows a similar result to the Gaussian smoothing in the spatial domain. One difference is that the computational cost of the spatial filter increases with the standard deviation (i.e. with the size of the filter kernel), whereas the costs for a frequency filter are independent of the filter function. Hence, the spatial Gaussian filter is more appropriate for narrow lowpass filters, while the Butterworth filter is a better implementation for wide lowpass filters.