You may want to try out the Lazarus project Transform.lpi that allows you to evaluate different spatial filters.
https://github.com/rordenlab/niimath/tree/development/srcIn the attached screenshot I show the provided 256x256 pixel zone plate reduced 50% in the horizontal axis. You can choose different Filters from the drop down menu: Bilinear, Lanczos Sinc, and Mitchell.
When you reduce the sampling rate of a signal, you need to be aware of aliasing artifacts: frequencies higher than the Nyquist can appear as lower frequencies. The zone plate is designed to exhibit this. Different filters have different behavior: the Mitchell and Linear filters do not preserve high frequencies, which makes them more resistant to artifacts, while the sink preserves higher frequencies but can show ringing artifacts.
If you want to avoid downsampling artifacts, you want to consider an anti-aliasing filter. My own Lazarus projects use the method of Schumacher that adjusts kernel size to compensate for downsampling, though an alternative approach is to blur the input image at the Nyquist.
https://github.com/erich666/GraphicsGems/tree/dad26f941e12c8bf1f96ea21c1c04cd2206ae7c9/gemsiiiIn theory, you could apply a unsharp masking method to enhance these artifacts if you find them useful, though you may find a specialized edge detector better suited. Simple edge detectors include Sobel, Roberts cross, and Prewitt. More fancy edge detectors include the Canny method.
Also relevant:
https://nbviewer.org/urls/dl.dropbox.com/s/s0nw827nc4kcnaa/Aliasing.ipynbhttps://neurostars.org/t/downsample-image/16677/7