Interference Removal from Time-Frequency Data

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:

1. To take an FT of the image itself.
2. Remove the high-frequency features (those belonging to fast-changing features.
3. For all pixels, use a large (21x21 pixel) filter to compute mean, median and rms of grey levels.
4. If the pixel value is greater than (mean+5*sigma) or (median + 5 sigma), then one can safely substitute median for the pixel value.
5. One could compute rms over the entire image to avoid being dominated by local features.

2008: Plans and Actions

Order not so crucial, but wish list is quite doable, right?

1. Get SAX paper out of the door.
2. Finish analysing MHD cube, how many chimneys will i lose due to sensitivity and resolution limits? Can we count holes in HI for a large number of MHD simulations?
3. B0809+74, what is the circulation time?
4. B0834+06, what is the problem with the circulation time at 35 MHz? How does the polar pattern look at higher frequencies?
   
5. 2-antenna interference on 3/4 major sources: from amplitude/ phase calibration of fringes, can we tell the sizes of sources with fringes observed?
   
6. Can we make crude images with even 10 different u-v tracks with a 2-antenna telescope?
7. Can we use the Moon for occultation studies?
8. Astrophysics - II course.
9. Meditate in Leh.
10. Maha working towards her higher studies abroad.