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| | 3 | The notation used below is that FFT(g(x,y)) = G(U,V). |
| | 4 | |
| | 5 | The following image shows the results of playing with my test FFT filtering code. The top left panel shows the original overscan subtracted science image. The top central panel shows the results of a 3-sigma clipping that reduces the power of the deviant frequency by a factor of ten. This clipping is performed for U > 100, V < 3. There are some minor changes, but most of the noise remains. I'm unclear at this point if the issue is the clipping threshold or the power reduction factor. The top right panel applies a harsh clipping, setting the value of all components in the clipping region to 0.0. This clearly removes more of the noise, but is likely excessive. In addition, corner glows have a banded nature introduced. This probably suggests that this algorithm should be applied post-DARK calibration. |
| | 6 | |
| | 7 | The bottom row shows a series of "obvious mistakes" that can be generated with this kind of filtering. All used the harsh clipping described above. The bottom left panel shows the results when U is clipped down to U > 11. This clears up a lot of the noise, but also introduces the "butterfly effect" that has been seen with the PATTERN.ROW results. This makes sense, as PATTERN.ROW is effectively removing all information around V~0, including the portions that define the PSF shape. The bottom middle panel shows a similar but decreased butterfly issue where U > 33. Finally, U > 100, V < 50 is shown in the bottom right. This is similar to the top right panel, but by extending the clipping in the V dimension, we create a checkerboard ringing. |
