Applying a Filter Kernel Defined by a Continuous Equation in the Frequency Domain I have a filter kernel $K(\omega)$ completely defined in the frequency domain as a continuous function of angular frequency $\omega$. I know $K(\omega)$ defined as a continuous mathematical equation.

Using the discrete FFT and IFFT, I would like to apply this kernel to a discrete time-domain signal using convolution via multiplication in the frequency domain. 

What is a good way to deal with this convolution?  Should I generate the $U(\omega)$ as a continuous function in the frequency domain, take the IFFT, and then truncate, shift and window the signal as mentioned in the book Steven W. Smith - The Scientist and Engineer's Guide to Digital Signal Processing? 

Alternately, should I take the continuous inverse Fourier transform of the function $K(\omega)$, and generate $k(t)$ completely in the time domain as a discrete signal by evaluating $k(t)$ for discrete values of $t$?  Then $k(t)$ can be treated as a FIR filter kernel and taken into the frequency domain.

Although this is a discrete 1D signal, are 2D image filtering kernels generated in the frequency or spatial domains?  What is the difference between the application of the kernels in the time and frequency domains?
Given an Image $ I \in \mathbb{R}^{m \times n} $ I would solve it as following (Using the Convolution Theorem):


I would sample the function in Fourier Domain into a grid of $ m \times n $.
I would apply DFT transform on the image to get the $ m \times n $ representation in the Fourier Domain.
I would apply Element Wise multiplication between the 2 $ m \times n $ arrays.
I would apply Inverse DFT on the result to get the filtered image in the spatial domain.


Pay attention that this will result in Cyclic Convolution and not Linear Convolution (Which requires more padding).