hiclean

hiclean implements Alan Delamere's version of HiRISE image radiometric calibration as coded in his IDL program called clean.pro. Mike's general approach is to use the calibration data acquired for each image and compute statistics from it used to characterize instrument conditions at the time the image is acquired. This data includes the reverse clock, mask, buffer and dark current data that is stored in Isis objects within all HiRISE Isis cubes. These objects are sometimes referred to as Binary Large OBjects,

Excerpts from Alan's description of his algorithm:

Cimage_mask,image,info
Step 1. Average all the usable masked pixels in each column including buffer and
 dark for the 18 binned lines for bin 1, 9 lines for bin 2 and 4 lines for bin 4
. Floating point array of 1052 columns for bin 1, 540 columns for bin 2 and 284 
columns for bin 4.
Step 2. Subtract this line from every line in the image.

Cimage_dark, image, info
The dark region has had subtracted from it the dark current generated in the mas
k area by the above procedure. It should be a nominal zero value except for drif
t and temperature change during imaging
Step 1. Average the last twelve columns of each line and produce a floating poin
t array of n lines long.
Step 2. Apply an 11 point smoothing to this dark array. [I have tried a number o
f different smoothing approaches including using median and averaging of differe
nt lengths. For the recent dark images the 11 point smoothing produced the minim
um observable degradation.]
Step 3. Subtract the smoothed value from each line in the array.

The actual code used is given below.

What are the residual errors from this method?
1.      The masked area pixels are closer to the hot amplifier than the image pi
xels. The temperature of the CCD will rise during imaging so insufficient dark c
urrent may not be removed.
2.      There is an unexplained negative value in the binned area prior to the d
ark area. This is the biggest error. For bin 2 it is about 15-30 DN.
3.      Other?

Essential algorithm extracted from code below
  ave_lines,image,21,20+maskbinend,masked       ;All columns in masked
for I = 0L,nlines-1 do a(0,i)=image(*,i)-masked         ;all columns have masked
 subtracted
ave_cols,image,nc-12,nc-1,dark
dark   = smooth(dark,11)
for i  = 0L,nlines-1 do a(12:(nc-17),i)  = image(12:(nc-17),i)-dark(i)*bin
;Note bin not bin^2
for i  = 0L,nlines-1 do a(0:12,i)  = image(0:12,i)-dark(i)
for i  = 0L,nlines-1 do a(nc-16:nc-1,i)  = image(nc-16:nc-1,i)-dark(i)
        

In addition to those changes, some consideration has been given to situations where bad missing (null) data exists in the mask and dark calibration data. Bad data could occur in these areas when gaps occur in the dowlink of the file from the spacecraft or other anomolies. Also, LookUp Tables (LUTs) can cause data saturation if they are not carefully considered prior to use.

In cases of missing data in the mask region, the minimum and maximum of the data after averaging each column is computed. For low saturated and null special pixels, the minimum mask average value is used. For high saturation special pixels, the maximum is used.

For the dark pixel calibration data, an 11 pixel wide filter is applied after averaging each line in this region. This replaces any missing data with the average of the 11 pixel filter. If data is wider than 11 pixels in this data, nulling of image data will result.

Under any condition valid data is nulled due to insufficent calibration data in either region, hiclean terminates with and error. The resulting output files is retained, however - just in case it is useable.