Vaishnavi worked on Median-based interference identification and removal. The basic idea is to treat the time-freq map of a given baseline as a 2-D array. See the above diagram for such an example, where the red arrow indicates one interference location.
We then take a small section of this 2-D array (say 32x32 matrix), where the 3rd axis is the intensity (or amplitude). We compute the median and standard deviation of the section (32x32 matrix). We then put some criterion of (median+7*sigma) for genuine data.
Amplitude > (median + 7*sigma) is treated as interference. This appears to identify interference quite reasonably. Check the following image, where black pixel indicates interference. Compare that with the top image, where most of the interference is identified.
There is some data loss due to over-correction. Even so, total data flagged is about 15%, which is quite good.
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