We start with a flat time-frequency data, much like shown in the image on the left. This is VLA data (one baseline) observed at 325 MHz. The arrows indicate high-level and low-level interference in the data.
The most striking quality of interference, is that it is mostly confined to high 'spatial' frequencies. Here, I refer to spatial grey-level variation frequencies.
There are two approaches to remove / isolate such an interference:
- To take an FT of the image itself.
- Remove the high-frequency features (those belonging to fast-changing features.
- For all pixels, use a large (21x21 pixel) filter to compute mean, median and rms of grey levels.
- If the pixel value is greater than (mean+5*sigma) or (median + 5 sigma), then one can safely substitute median for the pixel value.
- One could compute rms over the entire image to avoid being dominated by local features.