# Montage Cookbook: Data Stretches

mViewer supports several data stretches. In the following, you can click on a command to see the results of its stretch. The simplest possible stretch processes the image into 256 bins equally spaced between the minimum and maximum data values. For astronomical images this is almost always a bad idea: mViewer -ct 1 -gray fits/SDSS_r.fits min max linear -png min_max_lin.png The reason for this is that astronomical images usually have most of their pixel values clumped near the low end of the data range (and often all "in the noise"). The stretch can be improved by applying a non-linear stretch binned in the log of the data value (or other functions like sqrt(), which we do not support): mViewer -ct 1 -gray fits/SDSS_r.fits min max log -png min_max_log.png Many images have extremely high dynamic range, so even log() may not be enough. mViewer supports log(log()) and higher variants: mViewer -ct 1 -gray fits/SDSS_r.fits min max loglog -png min_max_loglog.png This tends to de-emphasize the low-level structure to the point where much of it vanishes. That may be a good thing but often is not. So mViewer allows various ways of setting the range of the binning. For instance, stretching from the first percentile in the distribution to the 99th yields: mViewer -ct 1 -gray fits/SDSS_r.fits 1% 99% log -png 1_99_log.png Of course, to get percentiles you need to generate a histogram. For a lot of astronomical images this will look like a gaussian (the noise or real low-level structure) and a very long positive tail. It also means we can couch levels in terms of "sigma" values: mViewer -ct 1 -gray fits/SDSS_r.fits -2s max log -png 2s_max_log.png With the histogram we could also stretch in terms of a histogram equalization. However, with the bulk of the distribution being noise/low-level structure, this doesn't usually look very good. Instead, we perform a gaussian histogram equalization which is based on the error function (erf()) rather than the histogram itself: mViewer -ct 1 -gray fits/SDSS_r.fits -2s max gaussian -png 2s_max_gaussian.png Again, the emphasis is often much more on the values in the long positive tail, so we also support distorting the above with a log transform: mViewer -ct 1 -gray fits/SDSS_r.fits -2s max gaussian-log -png 2s_max_gaussianlog.png This last stretch is will do a creditable job on almost any image. In fact, the only variation that usually needs to be made is the the lower limit: -2σ works well where the low values are real structure and higher values (up to about +1σ) may be more appropriate the more the low-level structure is noise. Also available is a sinh() stretch. Others could easily be added. The default image for this page combined a stretch with a "pseudo-color" color table: mViewer -ct 4 -color ffffff -label 100p 50p "50% max gaussian" -gray fits/SDSS_r.fits 0s max gaussian -png colorStretch.png The mViewer stretching has one more variant. Sometimes one has a set of images that all need to be stretched the same way. For instance, you might have a set of adjacent tiles that are kept as separate files rather than being coadded into one giant (an unmanageable) single image. You still want to display them (perhaps side by side) as if they were part of a single whole. Alternatively, you might have a collection of galaxy image that you want to view on the same scale and with the same stretch. Normally, mViewer analyzes each image separately, resulting in very different stretches. To get all the images on the same stretch (especially the data-dependent gaussian histogram equalization) we can generate a histogram (based on whatever image you wish to use as reference) using the Montage mHistogram module. This uses the same algorithm as mViewer but writes the results to a file. mViewer can the be told to use this histogram file rather than generating one at run time. This is done inside the "-gray" (and "-red", "-green", "-blue") directive using the following construct: Instead of, e.g., "-gray image.fits -2s max gaussian-log" Use "-gray image.fits -histfile ref.hist" The same histogram file can thus be applied to as many images as you need. | Data: SDSS_r.fits