Histogram equalization (or stretching) of RGB picture in python

Question

I am creating automatically JPG pictures from multispectral data. Created picture is very dark. So I thought it would be best idea change brightness (like Image.Enhance in PIL). But there was a problem, because some pictures need more brightness than others.

So next idea was try linear stretching of histogram. So I created script which iterate over RGB tuples and compute new intensity for pixels. There was very small difference. Probably because the range of values was everytime 0-255. Then I tried histogram equalization (ImageOps) for R, G and B but the result was no good, please see middle part of picture. I found on the internet that this is not good approach because colors can change dramatically. It is probably my case.

The best idea looks convert RGB array to HSL and then change luminance but I can't use constant for maximize Luminance because pictures are different and need different constants for. Should I use histogram equalization on Luminance or what is the best approach how stretch or probably better histogram equalization of my picture?

I am looking for something like Image/Auto adjust colors in IrfanView or in some SW are used name Linear Normalization...

I hope that picture will be help to you understand my problem. I probably choose bad way how to achieve my goal.

Thank you for any answer, I will be very glad.

EDIT

Left image for download

Next images I can upload later, today.

Hey @Bulva, do you have an image sample to make possible some tests? — Eliezer Bernart, Jan 29 '16 at 18:19
Yes, I have about 10 pictures. So if you have any idea I can test it on them — Bulva, Jan 29 '16 at 18:39
@Bulva Probably he wants that you share some of these images in order to make experiments — MBo, Feb 02 '16 at 05:32
Sorry about it, my mistake. I will upload some pictures today — Bulva, Feb 02 '16 at 08:14
I added the left image from preview in my post. Next images I can upload later, if it will be necessary. Thanks for help — Bulva, Feb 02 '16 at 09:52

score 1 · Answer 1 · edited May 23 '17 at 11:53

I would suggest proceeding with the same approach as you have stated with slight modification.

Convert the RGB image to LAB image.
Apply localized histogram equalization to the L-channel.
Merge it back with the other channels.
Convert it back to RGB image.

You can check my answer for this in a different question here:

The code I have there is written for OpenCV using python. You can modify it for C language if you wish.

Let me know if it has helped you!!

score 0 · Answer 2 · answered Nov 14 '16 at 09:30

I am not sure if this applies, and I have not applied this myself, but I was reading on this article about underwater contrast stretching: http://www.iaeng.org/IJCS/issues_v34/issue_2/IJCS_34_2_12.pdf

What it suggests might help

"In order to address the issues discussed above, we propose
an approach based on slide stretching. Firstly, we use contrast
stretching of RGB algorithm to equalize the colour contrast in
the images. Secondly, we apply the saturation and intensity
stretching of HSI to increase the true colour and solve the
problem of lighting"

Histogram equalization (or stretching) of RGB picture in python

2 Answers2