[sane-devel] Dust removal inSANE ?
rolf at bensch-online.de
Tue Jun 25 19:23:17 UTC 2013
I found this thread in the SANE mailing list archive.
I already implemented ir scan for my CS9000F (pixma backend).
Now I'm searching an open source program and/or lib for processing ir data
against the rgb scan. Fortunately my scanner provides pure dust images without
any shadows from the negatives/slides.
Did you started with implementing your code in sanei and/or can I reuse your
code from /http://wwwuser.gwdg.de/~mrickma/sane-proscan-7200/status-110711/ <http://wwwuser.gwdg.de/%7Emrickma/sane-proscan-7200/status-110711/>/
and/or do you have newer code and/or can you please provide your experiences
with dust removing?
Many thanks for your help in advance.
Am 11.07.2011 17:03, schrieb m. allan noah:
> Excellent description of both the problem, and the solution. I wrote
> sanei_magic to contain exactly these sort of routines. Though, you
> could also add a sanei_ir or some such, if you want a more 'private'
> playground :)
> On Mon, Jul 11, 2011 at 12:54 PM, Michael Rickmann <mrickma at gwdg.de <http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/sane-devel>> wrote:
> >/ 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/. <http://wwwuser.gwdg.de/%7Emrickma/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
> />/ --
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