I have two sets of photos, each taken from two different Machine Vision Cameras. To keep the noise level same in both photos, I have to set the R,G, B gains to the same level. But the problem with that approach is that, at unity gain (Gain = 1) the photos look very different for the same subject (a Gray card). In essence, the color temperature is different for the two camera systems and needs to be configured to the same temperature. Any idea how that can be done ?


PS: I do not want to white balance the photos. I want to keep the color of the white point same between two different photos.

Adding some more information. I have done some preliminary tests on both systems. First, I have fixed all R,G,B gains at 1,1,1 and taken a photo of a Gray card. Then, I have progressively increased the gains of R from 1 to 1.1,1.2 and so on, upto 2. Same for G and B. The excel sheet attached contains the means of the R,G and B values and the grey box is the corresponding Std. deviation. The same is done for the other machine.

Excel Sheet with data

  • \$\begingroup\$ You don't mention what software you have available. If the object is the same in both photos this is easily done in Adobe Lightroom for example. Currently I think the question could be a. rather broad and b. opinionated on what software to use, however i'm not voting down as it's still a good question. \$\endgroup\$
    – Crazy Dino
    Commented Oct 6, 2016 at 8:50
  • \$\begingroup\$ @CrazyDino What I am looking for is an algorithm. Yes, I can make the object on both ends the same and I think I can get access to Photoshop and Lightroom but the issue is that putting a manual element (importing into PS/LR) is not what I am looking for. \$\endgroup\$
    – Roy2511
    Commented Oct 6, 2016 at 9:46

1 Answer 1


You can't have everything :-( . To change the color temperature, you're going to have to change the polynomial you apply to the R,G, and B data.

It's not true that you can keep the noise level the same simply (and only) by making the gains identical. Clearly since the final images differ, you've got different responsivity and/or gains in the two cameras right now. Before you get too deep into generating the polynomial corrections, start by comparing the mean and standard deviations of the color channels between the two cameras. For example, if the two red channels R1 and R2 have a different mean value, then you probably want to adjust the gain in postprocessing. If they have significantly different standard deviations, then you may have a nonlinear gain problem.

  • \$\begingroup\$ Okay. But the only non-linear mapping I have applied to my data is a gamma correction. \$\endgroup\$
    – Roy2511
    Commented Oct 6, 2016 at 13:47
  • \$\begingroup\$ Also, isn't it possible that the difference in images is due to LEDs of different color temperatures? Also, I am updating some revisions to the question and uploading an excel workbook. Hopefully it will give you some more insight. \$\endgroup\$
    – Roy2511
    Commented Oct 6, 2016 at 13:55
  • 2
    \$\begingroup\$ @Roy2511 You made no mention of a different lab setup - just a different camera. If you have two completely different test stands, then certainly you'll want to try to make the illumination identical in both. Otherwise you are violating a bunch of Test&Reliability rules. \$\endgroup\$ Commented Oct 6, 2016 at 17:20
  • \$\begingroup\$ The LEDs which are illuminating the subject are from the same manufacturer. I have used a lux meter to keep the brightness same in both cases. Other than this, I am not sure what else I can do to ensure that the illumination is the same. \$\endgroup\$
    – Roy2511
    Commented Oct 7, 2016 at 1:55

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