Improvements of RAWTRAN

While work on processing of set of images of Venus setting (I will it show soon), I found important bugs and revealed some possible improvements in rawtran utility. Moreover, I also found that only the type zero (default) conversion has worked correctly for all versions up to today. The default choice have composed four RGGB pixels of the Bayer color mosaic to one big grayscale pixel by use of unique weights for separate colors.

The Bayer mosaic is defined as red, blue and two green pixels arranged to rectangular matrix. It means that the number of different colors is tree but we have four pixels on detector matrix. Two green pixels are reserved for single color. Now, we have two choices for output intensities: sum of the pixels or their arithmetical mean. The sum gives precise value in integer numbers but one is two times greater than value of other colors. Opposite with this, the mean gives values of the same order as the red and blue pixels unfortunately with a rounding error as result of conversion between real and integer numbers. I think, the use of the mean and real numbers in FITS should be considered as a smooth alternative when a lot of disk space is available. Also sum of two greens and double values of red and blue can be a nice alternative for 12bit and 14bit A/D converters while we are storing data in 16bit FITSes. The arithmetical mean is the most simple alternative with small lost of precision. Please leave a comment about a preferred way to solve the controversial point.

Color weights

As the first noticeable change, I mention about the color balance (weights) used for conversion of a color based to a greyscale image. Generally, the conversion is done by the formula

I = w_R*R + w_G*G + w_B*B

where I is grayscale intensity, R,G,B are proper intensities in appropriate filters and w_i are a suitable weights. The choice of w_i depends on problem to solve. The setup of w_i is supplied by your camera according to spectral profiles of color device elements and to light conditions of an acquired scene. The photographers perhaps uses the term "white color balance" for the procedure, because it can be easy established by balancing of the coefficients on a white. Now, the camera setup is default. The previous versions used w_i = 1 without possibility to specify by hand. Note that both setups are useful in different situations. The camera supplied setup is ideal for images represents intensities close to reality. The 1's weighting can be useful for detailed spectral examination of images.

The color weights are derived from camera supplied data in field "Camera multipliers". The four numbers B1 B2 R1 R2 are used to estimate of color balance as:

w_R = R1/R2,
w_G = 1,
w_B = B1/B2.

The green band is supposed as basic unit and both remain are relatively scaled. The numerical values agrees with ufraw data. I don't know why also provided "Daylight multipliers" are not used for the reason.

For type zero the image is grayscale and the value is indicated by keyword FILTER which is set to GRAY. Weight factors are not directly included in keywords.

Multi-color fits

The most important change in the code is connected to multi-color (band) fits images. Previous versions has been badly decoding of Bayer's mask (the colors has been swapped). I corrected the bug and changed structure of resultant FITS files.

The effect of correct separation of colors can be easy showed on the image of the spectrum of a white source (data projector) observed through a rainbow glasses (famous paper - diffraction foil glasses). Look on included image for details. I'd correctly decoded the structure of image color mask on the image.

Color image and separated color bands.

Since recent issue, the multi-color FITS has following structure: the dummy primary image array (the header without any data) and tree color (band) extended images. Every header of the extended data contains a new keywords FILTER indicating one from set RED, GREEN, BLUE and appropriate color weight factor included as CBALANCE. Other new keyword EXTNAME is the same as FILTER and can be directly used to select the filter. For example, with ds9 you can directly select blue band by the command:
bash$ ds9 x.fits[BLUE]

The same selection can be done also as x.fits[3]. The file names uses the extended file name syntax introduced by cfitsio library.

Note, about multi-color (band) FITSes. The packaging of more images to one file is frequently used in space astronomy, where an experiment on a spacecraft records a radiation on many different wavelengths. The color mode is particular case of the gadget. The idea can be also extended for including of correction or calibration images or data.

Grid mode

The grid mode (type 1) simply arrange of pixels without collecting or space separating of pixels. I have no idea about any usefulness of the mode. Also I don't fully understand why noise (dark) level and white (saturated) regions are correctly displayed but the mid-level intensities produces grid structure.

8,16,32,-32 bits per pixel images

The output FITSes can have specified bitpix parameter (see man page). It can be very useful when you wanna save fine precision of intensities. The non-integer values are common product of white balancing when type zero conversion is selected. Also, the problem extensive discussed above is suppressed.

I suppose that the mode 16-bit is perfect for storing of raw data, the mode -32-bit for further processing and modes 8 and 32 are unusable. Images with 8-bit depth will usually white due to overflow of image's values over 255. The 32-bit mode only grows range of integer numbers and practically only occupied twice more disk space without any growing of stored information.

Examples of conversion

Basic run (equivalent commands):

bash$ rawtran IMG_666.CR2 > IMG_666.fits
bash$ rawtran -o IMG_666.fits IMG_666.CR2

The produced image IMG_666.fits contains grayscale image with half image dimensions of the original. Please, respect rule: first options than an image to convert.

Process all images in directory:

bash$ for A in *.CR2; do rawtran ${A} > ${A%CR2}fits; done

Process all images listed in a file l.lst:

bash$ for A in $(cat l.lst); do rawtran ${A} > ${A%CR2}fits; done

The file l.lst is usually created by command: bash$ ls > l.lst

Produce of multi-color image and show its green channel (band) in ds9:

bash$ rawtran -t 2 IMG_666.CR2 > IMG_666.fits
bash$ ds9 IMG_666.fits[GREEN]

Produce user's white-balanced multi-color image:

bash$ rawtran -t 2 -w 2.0,1.0,1.5 IMG_666.CR2 > IMG_666.fits

Separate blue color (band) from an image:

bash$ rawtran -t 3 -c B IMG_666.CR2 > IMG_666.fits

The output file roughly corresponds to a filtered CCD image with blue band.


Please, read source code of rawtran.c to get more detailed understanding of my ideas. I know that code is wrongly structured, it uses of poor data structures etc. but it isn't important for me. Valgrind shows defect free code.

I'm little bit confused about handling with output file. The default setup writes data on standard output which can be strangle for unexperinced users. The suffix swaping is not too elegant for C coding (shell replaces strings more effectively) and unique output filename like rawtran.png is confusing too.

All developing is proceed on images produced by Canon 30D. Be careful when your are working with another digital camera.

Download of RAWTRAN.
An older post about RAWTRAN.
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