Schur's LRGB Color Correction Technique

A schur fire method for true color LRGB astronomical images

By Chris Schur and John Ofarrell

Uploaded 1/15/06

Note: I have intentionally boosted the saturation of some of the images beyond normal amounts to better demonstrate the concepts herein.


 Here we will analyze the color distortion that occurs to the color image data when we produce an LRGB image, and demonstrate a technique to rebalance the LRGB back to the original precise G2V calibrated RGB image.


As astro-imagers, we constantly push our cameras and gear to their limits to record highly colorful and detailed images of faint deep sky objects. There are two primary techniques used today for producing color images but is missing the connection between them which must exist to produce the most accurate and color correct photographs. The first of these is what is called "G2V calibration". While you can read about the details of doing such a calibration here on Al Kelly's web site, for this discussion we will first concern ourselves with why we use this, and second, how to apply this accurately produced color image to our second most popular technique in the imagers toolbox: The LRGB color composite.

Why G2V Calibrate?

Anytime we produce an image with a digital sensor, we take three images, each one filtered with a different standardized color: Red, Blue and Green. These images are then combined in software to produce the final color image. But here's the catch - unless we standardize on what we consider to be a white point, the three images can be combined in any number of mixes and the colors will not be right. Astrophotographers have chosen the color of our sun for the white point, since it is central in our eyes color vision response, and it appears as pure white to us in the daytime, when at the zenith. (The sun is not yellow, take a sheet of white paper and put it in the sun. What color is it?) Rather than go into a lengthy discussion on why the G2V star such as our Sun was chosen as white, I will say that each color gamut available such as Adobe RGB, or LAB color space uses a different white point and we must compensate. By using a standard candle so to speak for color, we can now compare our images taken over large areas of the sky and the spectrum of colors recorded by our cameras will look similar in the final images. So my image of the Trifid nebula will look just like yours, except for size. Direct comparison with a common standard.


Soon after the widespread use of color filters in CCD imaging was started back in the 1980's, it became painfully apparent that long exposures were needed through the dense color filters to get dim nebulosities to show up. Producing a direct RGB image was not only extremely time consuming, but also the long exposures made for tracking errors, and focus shifts with amateur grade equipment. The solution that emerged, and is now commonly used today for many deep and detailed amateur shots is the technique of LRGB. Here we combine a deep black and white full spectrum image with color frames taken with the camera binned 2x2 or more to drastically shorten the total exposure time for one subject. There are many sources to read about producing LRGB images, for example one of the best is Ron Wodaski's Advanced CCD imaging book. But there is a dilemma: The LRGB image is convenient to make, but not color accurate. So bad are the color distortions produced by introducing a new Luminance image into the LAB color space or by combining with layers as Luminosity in Photoshop, that they no longer resemble the original calibrated G2V data we so painstakingly produced.

A typical LRGB series

Below is an example of a typical LRGB composite. On the left is a deep Luminance image, that was binned at full resolution and has been processed to bring out the maximum detail. In the middle is the G2V calibrated color RGB frame, that was shot originally at 2x2 binning for maximum color signal. Crisp reds and electric blues define the beauty of this nebula clearly - but something happens to all LRGB composites as illustrated on the right. The saturation is obviously less, and the reds shift to the all to commonly seen (this is all too common, unfortunately) salmon pink hue. This is where most imagers stop, and consider the image done. Obviously this does not resemble the right colors we started with!


Normal Method of making LRGB image: L + RGB = LRGB ( --> Salmon Pink !)

 Understanding and Rectifying the Problem

At the end of this article John has put together the details of the mathematics of producing the LRGB "Lab" composites and the color shift issues. Here I will summarize the details of both the color and saturation shift, and during our analysis a simple solution will be presented.

Where the Color Shift Comes From

When you combine a Luminance frame into a color RGB data set, you will pass through Lab color space. A direct method is to simply convert the RGB data that has been scaled up in size to Lab color space, then replace the "L" channel with a new highly detailed deep frame. Alternately you can paste the new Luminance frame right over the RGB data, and combine in layers with Luminance. Or the third way is to paste the RGB image over the new Luminance frame, and combine in layers using Color. They all come out the same because Photoshop does the mathematics internally using the Lab color space. In Lab mathematics, it can be shown that the three channels are not completely separate. The "a" channel is tied loosely with the L channel, but the "b" channel is not significantly connected. The result is that if you modify the L channel only, you will still change the "a & b" channels proportionately. Now the "a" channel controls the reds and oranges, and the "b" channel the greens and blues. Since the "b" channel is not significantly altered, it does not change hue as the brightness of the L channel goes up. The "a" channel is a different story! As you increase the brightness of the L channel, the "a" channel shifts its hue. The result is that reds become more orange resulting in the infamous "salmon pink" coloration, while blues stay pretty much the same. This is what the math predicts, and we will see in the examples below, that it bears out well in an actual astronomical image.

Where the Color Saturation is Lost.

Unlike the hue shift which plagues only the "a" channel, as the Luminosity in the "L" channel increases, both the "a" and "b" channels will loose their saturation. Because this is a non linear function, its is quite complex to predict a correction factor. Just turning up the saturation in a standard image processing software will only do a partial job. You can visualize the loss in saturation as the whites start replacing the colors at high brightness. The brighter the L channel gets (or any RGB image), the greater the loss of color saturation and contrasts. Many imagers try to compensate the salmon pink shift in hue by simply turning up the saturation. You will see below that this is quite unsatisfactory as well.

Demonstrating the changes with CM

First I will introduce you to a new imagers tool, created by professional photographers to better understand and change the color tonalities in their photographs. The version I am using comes standard with the full Picture Window Pro software, but the Color Mechanic is available separately as well for a lot less money. You can download a free evaluation copy from and try it for yourself.

 The "Color Mechanic" Color Hexagon

On the left is the color analysis and correction diagram of Digital Light and Color's Color Mechanic. I have never found anything like it that does color adjustments and changes such as this amazing tool. Here's how it works, since you will need to understand it in the next steps. The very center is pure white - the sum of all colors equally. Three of the corners are the main RGB colors we are familiar with in normal color work, and the other three corners are exact even mixes of those three producing Cyan, Yellow and Magenta. Full saturation is in the corners, and as you go toward the center, the saturation decreases linearly. As you move in a radius about the center white point, you shift the Hue, or color frequency. When you click on a part of an image, a small circle appears on the diagram corresponding to its hue and saturation, allowing easy comparison of changes in our images.

Now that you have a good feel for this tool, lets move on to analyze what happens when you make an LRGB astronomical image.

 Both a saturation and hue shift can be seen for the red and only a saturation shift for the blue nebulosities.

By clicking on the same exact spot on both images for both the red and blue nebulosities, the true color shifts can be seen. In the red part of the graphic at the top, the shift from RGB to LRGB is shifted in both hue and saturation. You can see the LRGB is shifted more into the oranges. If it had been saturation only, the shift would be outward and inward toward the center. The blues on the other hand are nearly unchanged in Hue, but affected about the same as the reds in saturation. You can see if you were to move the Blue RGB point directly inward toward the white center of the hexagon, you would end up on the LRGB data. So as the math suggests, reds are shifted both in hue and saturation, and blues and greens will not change much in hue, yet be shifted in saturation as well. So correction for the warm tones is a two step process: First you correct the hue, then the saturation. You will still want to do this to reflection nebulosities because often their is a yellow or brown color to the dust, and of course the orange stars will be corrected as well.

  Original LRGB

  Look VERY carefully, its pinker.

 Hue correction of LRGB on the left using layers in Photoshop and combining with Hue, yields image on the right.

The first step in correcting the LRGB image back to its G2V balanced origin, is to correct the Hue. To do this is very easy. Layer your calibrated RGB data (registered!) over the LRGB composite. Next as seen in the center image above, select to combine the RGB image with Hue at 100 percent transparency. The shift will be dramatic for some images, less so for others. But in all cases - the new image will have the correct hue of the original RGB. In fact, this simple method you can do at any time during your normal color processing to always bring it back to the standard color.

 The comparison shows the red has now been Hue shifted in frequency to match RGB.

Now lets take a look at what happed on the CM diagram. While the blues are about the same, the reds took a dramatic turn and now line right up with the central white zone. The beauty of this technique is that ALL colors in the image will be corrected irrespective of their end hue. Now you can see from the diagram, the only task left is to correct the saturation.

 Next we implement the Hue Curves in PixInsight. This is not your typical saturation boost! The curve boosts saturation based on the existing amount of saturation, so areas with low saturation such as dim nebulosity, and the bright core area will be enhanced the most. Its really a wild concept. This takes care of the change in saturation with increasing luminance which is non linear.

Remember we discussed how the saturation in an image is not linearly proportional to the brightness increase of the L channel. Rather than a simple brute force approach of cranking up the color saturation and getting really poor results - which you will see demonstrated below in a bit, we apply a special transfer function - a simple gamma curve - to the saturation. Pix Insight is the only program I've seen that allows this to be done this way. (And its freeware). To under stand just what happened here, think of the plot above as no saturation on the lower left, and increasing to maximum saturation as you go right and up. You start with a straight line from the lower left corner to the upper right which maps 1:1, just like a standard curves transfer function. Now when we change this to a curve, the low saturations get boosted along with the midranges. The top end doesn't change much. So we can keep the saturation under control and give it where it needs in - to the brightest areas that have lost the most. The dimmest areas are also low in saturation and get a good boost too. You can really see the blue come up wrapping around the red part now.

These two images show the difference between a normal Photoshop saturation boost, and the Pix saturation curves boost.

 The left image is why you cant simply turn up the saturation normally. Note the ring of intensely saturated reds around the edges of nebula. They are maxed out and starting to red clip. But the center of the nebula is still not nearly as a saturated red as the original RGB data suggests. Thats the non linearity of this creeping in. The right frame was corrected with the saturation curve tool in PixInsight. No red fringe of over saturated color.

The final acid test - the original G2V calibrated RGB and the new LRGB with correct color and much more structural detail.

You can see here the pair of images now have overlapping samples when comparing the original calibrated RGB image on the left to our finished LRGB on the right. Both the reds and blues now line up perfectly. The final LRGB now has stunning high resolution detail AND correct colors. It is our hope that we can change your thinking on processing LRGB images to a whole new level now. Don't drop the ball at the finish line - go all the way and get that color right on target !

LRGB Mathematics (John Ofarrell)

Many amateur astrophotographers have experienced color shifts in red nebula when using the LRGB method of enhancing their images. An RGB image is made and converted into Lab color space. The L channel is removed and replaced with enhanced data. The color of the resulting image doesn't match the original RGB data. Why is this? The answer lies in the details of the RGB to Lab transformation. Since there are many flavors RGB color space, we will use Adobe RGB (1998) color space for our discussion. An RGB color space is defined in terms of red, green, and blue color space primaries plus reference white and black points. These five quantities are defined by X, Y, and Z coordinates in CIE 1931 color space. The process for converting RGB to Lab starts with aconversion of RGB to CIE 1931 XYZ. The XYZ color
coordinates are then converted to Lab. If one usesthe information in "Adobe RGB (1998) Color Image Encoding" document an expression for L in terms of RGB can be calculated. For the remainder of this discussion we will assume that RGB is in the form of 24-bit color with R, G, and B being represented by 8-bit values. If we make a simplifying assumption that the reference black point is X=Y=Z=0 then L as a function of RGB is as follows:

L = 116 [ 0.29734 R' + 0.62736 G' + 0.07529 B']^(1/3)- 16

where R' = (R / 255)^(1/gamma), G' = (G/255) ^ (1/gamma), B' = (B/255) ^(1/gamma), and gamma= 2.19921875.

The value of L can range between 1 and 100. Only for R=G=B=255 is L equal to 100. So a saturated pixel in an L channel is white no matter what was in the original RGB. Consider a pixel in an RGB image that is pure red (R=255, G=0, and B=0), conversion to Lab gives an L of 61. Thus any pure red pixel in the original RGB image that has its L value replaced by a number greater than 61 has to change color. So why doesn't the color just shift toward the white? Why do
images end up with a "salmon pink" color? There is no simple explanation. Salmon pink is essentially pink with a little extra green added. Where does the extra green come from? One can see from the equation for L as a function of R, G, and B that L has the greatest
dependence on G. It seems reasonable that G will be most affected by changes in L. All of this can be verified by setting up a spread sheet that converts RGB to Lab and back again. The information needed to convert Adobe RGB (1998) to XYZ can be found at Adobe's website. The formulae for converting XYZ to Lab can be found at



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