This module estimates the background fluctuations in the input image(s). It is very similar to Detect MedFilter. It finds the 68-percentile range of the pixel values in a sliding window in order to measure the Gaussian noise.
INPUT
Window X: (int) The X size in pixels of the window used to compute the median of the background. The default value is 45
Window Y: (int) The Y size in pixels of the window used to compute the median of the background. The default value is 45
Number of Outliers Per Window: (int) The number of outlier pixels (N) being rejected from the X*Y window when computing the noise.
Minimum Good Pixels Per Window: (int) The minimum number of good (non-NaN, non-masked) pixels needed to compute the noise. If insufficient good pixels are present, the "missing" output pixel is replaced by the interpolation of its neighbors.
Minimum Good Neighbors Number: (it) The minimum number of good neighbor pixels need to perform the replacement of "missing" output pixels.
COMMAND LINE INPUT
&GAUSSNOISE
Window_X = 45,
Window_Y = 45,
N_Outliers_Per_Window = 100,
Min_Good_Pixels_In_Window = 9,
Min_GoodNeighbours_Number = 4,
Max_BadPixels_OutputImage = 100,
&END
if use_psp_to_detect is set in the APEX Settings and Input_Type = "snr_input" is set in the Detect module then you must also include the following parameter block. N_Outliers_Per_Window must be set to 0:
&PSP_GAUSSNOISE
Window_X = 45,
Window_Y = 45,
N_Outliers_Per_Window = 0,
&END
OUTPUT
Generated FITS (*_noise.fits): The output is the tile noise image(s) measured from the input image(s).
DISCUSSION
The program computes the Gaussian noise (68 percentile) for each pixel in the input image using a rectangular window of Window X by Window Y pixels. It is achieve by omitting the Number Of Outliers Per Window highest pixels from each median window. There is a minimum required number of good (not-NaN and not marked by any mask) pixels per median window Minimum Good Pixels Per Window. If the number of good pixels is below this threshold then the corresponding pixel in the output image is marked as a "missing" pixel. When the median calculation is finished, the values of the marked pixels are interpolated from the neighboring pixels for which the median has been found. In order to do so the program scans around the pixel in question and accumulates values of good pixels. When the number of accumulated values reaches or exceeds the minimum number given by the input parameter Minimum Good Neighbors Number, the program finds the average and stores this value as the median for the pixel in question.
