I learned from other posts in this list that SANE does not provide much support for infrared. In sane.h some related definitions are even commented out. Only in coolscan.c I have found some RGBIfix routines by Andreas Rick which must be related to what he describes on his page at http://andreas.rick.free.fr/sane/dustremove.html. With my attempts to support PIE film scanners in the pie backend I have reached a stage where I can receive R, G, B and I color planes at resolutions from 300 to 3600 dpi at 16 bit color depth. I wish to use the infrared channel for dirt removal without touching current SANE specifications. Essentially three things have to be done: 1) reduce red spectral overlap from the infrared (ired) plane 2) find the dirt 3) replace the dirt Everything beyond depends on the kind of film and taste. If you are interested in quick results skip down to the last paragraphs of this post. I rolled up my sleeves though in a different way than suggested on this list and made a small program ircleanest.c in which I tried to implement above three steps. For trying image calculations before programming "ImageJ" (http://rsbweb.nih.gov/ij/) has been great help.
Ad 1) Quite often the ired image looks like a greyscale image with dirt emphasized. Something similar was reported for the Epson V700 scanner in ( http://lists.alioth.debian.org/pipermail/sane-devel/2011-February/028149.html ). The ired plane of negative films usually only contains slight shades of the image but slide films may show a considerable amount of it. I tried something with gamma and linear operations what Andreas Rick describes for the Coolscan. I could not get it to work and I do not wish to craft parameters by hand for every slide. When plotting the ired value of 1000 randomly chosen pixels against the red value the relation ired = b + a * ln (red) always gave a good fit. So calculating an ired' = ired - a * ln (red) should clean the ired plane. It works. I randomly sample 2000 pixels, calculate the parameter "a" by linear regression from the ln (red) and ired values, produce an ired' plane and scale it between 0 and 65535. I also tried to include green and blue planes but there is a lot of calculation and no real benefit. A similar cleaning effect one gets with the relations ired = b * red ^ a and ired' = ired / red ^ a. This comes closer Andreas Rick's suggestion of applying a gamma, and the coefficients can also be determined by linear regression. Ad 2) First I tried static thresholds to find the dirt. I still use two of them, Otsu's and Yen's in M. Emre Celebi's implementation in the FOURIER 0.8 project ( http://sourceforge.net/projects/fourier-ipal ). Yen's threshold in this implementation assumes a bimodal distribution and was the best of the static thresholds I tried in detecting only dirt. I still use it to add large dirty areas. But soon I gave up detecting smaller dirt with static thresholding without user intervention. On my search for an adaptive threshold I stumbled over the MAD (median of the absolute deviations from the median) filter (Crnojevic V. (2005) "Impulse Noise Filter with Adaptive Mad-Based Threshold. Proc. of the IEEE Int. Conf. on Image Processing, 3: 337-340). It is an understandable paper describing an algorithm of rather low complexity. Median filtering, however, is rather slow. First, I replaced the first median filter step of the original paper with a maximum filter because the dirty pixels are always darker than the real signal. Then I managed to get images from the scanner at maximum resolution and realized some impulse noise. So I resorted to a mean filter. The second median I also replaced by a mean filter to reduce computation time. In spite of these changes Crnojevic's recommendations for the choice of the parameters "a" and "b" were still valid when scaled to 16 bit. In my ircleanest.c program it is the filter_madmean routine. Combining the madmean dirt mask with the one from Yen's static threshold gave a good representation of what I felt had to be removed. Ad 3) For Replacing the dirt I dilate the clean image parts into the dirty ones. As I wish to do that in one sweep several pixels deep I first calculate the Manhattan distance of dirty pixels to their closest clean neighbors and keep an index of these clean ones. The result is ok in general but looks funny when the original dirt was overlapping a region of high colour changes. So I adapt the dilated pixels by a mean filter to their new surroundings and replace them again. Clean pixels remain unchanged by this procedure. You find examples of my dust removal at http://wwwuser.gwdg.de/~mrickma/sane-proscan-7200/status-110711/. The ircleanest.c is in the files.tar.gz. All you have to tell ircleanest is the resolution at which the scan was taken. An approximately 5 year old Pentium 4, 3.40GHz needs about 14 secs to clean a 4979 * 3330 image (slide scanned at 2700 dpi) though gprof reports only 5.09 secs. A two year old Phenom x4 64-bit needs 7 - 8 secs with gprof reporting about 4.75 secs. Would code for dirt removal be acceptable in a SANE backend or in sanei_magic? Regards Michael
