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16bit greyscale image to heatmap

I'm working on a scientific imaging software for my university, and I've encountered a major problem. Scientific camera (Apogee Alta U57) at my lab provides images as 16bpp array - it's 0-65535 values per pixel! We want to keep this range, but in fact we can't display them on monitor (0-255 grayscale range). So I found a way to resolve this problem - simply to make use of colors, and to display whole image as a heatmap (from black, blue, through green and red, to pure white).
I mean something like this - Example heatmap image I want to achieve
My only question is: How to efficiently convert 16bpp array of pixel values to complete heatmap bitmap in c#? Are there any libraries for doing that? If not, how do I achieve that using .NET resources?
My idea was to create function that maps 65536 values into (255 R, 255G, 255B), but it's a tough job - especially without using HSV model.
I would be much obliged for any help provided!

Your question consist of several parts:
reading in the 16 bit pixel data values
mapping them to 24 bit rgb colors
writing them out to an image file
I'll skip part one and three and give you a few ideas about part 2.
It is in fact harder than it seems. A unique mapping that doesn't lose any information is simple, in fact trivial, just a little bit shifting will do.
But you also want the result to work visually, meaning not so much is should be visually appealing but should make sense to a human eye. so we need a mapping that has a credible yet large enough gradient.
For this you should experiment a little. I suggest to make use of the LinearGradientBrush, as I show here. Have a look at the interpolateColors function! It uses only 6 colors in the example, way to few for your case!
You should pick many more; you may need to go through the color space in a spiral..
The trick for you will be to choose both nice and enough stop colors to create a 64k large set of unique colors, best going from blueish to reddish..
You will need to test the result for uniqueness; in fact you may want to create a pair of Dictionary and Dictionary for the mappings..

Do something similar to Auto Tone of Photoshop with Aforge.net or c#

Im developing an image skin detection app.
But there is a problem with my camera, that try to compensate the light and the result image is bad, in most of cases i have a cold or warm effect on the image.
When i use photoshop there is the AutoTone function that normalize an image and reduce this problem.
With aforge i want to use HistogramEqualization() filter but the result is very bad:
// create filter
HistogramEqualization filter = new HistogramEqualization( );
// process image
filter.ApplyInPlace( sourceImage );
So my question is:
There is a function in Accord or Aforge to have the same result of the autotone of Photoshop?
If not, there is some library or script that let to do this?
Thank you all.

I use the LevelsLinear filter and base it on image stats:
ImageStatistics stats = new ImageStatistics(sourceImage);
LevelsLinear levelsLinear = new LevelsLinear {
InRed = stats.Red.GetRange( 0.90 ),
InGreen = stats.Green.GetRange( 0.90 ),
InBlue = stats.Blue.GetRange( 0.90 )
};
levelsLinear.ApplyInPlace(sourceImage);
You can play with the range to tweak the result.

You probably don't want to equalize the histogram, because as you see, a photo that wouldn't normally have much red, would have alot of red created and make it look nasty. Instead you probably want to examine for a bias to a hue that occurs almost everywhere. For example, your original photo probably had a bias towards blue in almost every pixel, and thus probably shouldn't be there. Look for a minimum bias and remove that amount everywhere.
A more practical solution is to experiment with the white balance setting on your camera to see what gives you the best result. Choosing the right preset, will leverage an algorithm that's probably as good as what you would write by hand. But maybe you are doing this as a learning experience.

bitmap interpolation

interpolation of bitmap:
I have bitmap of 16*16, i want to increase the size of the bitmap to 160*160, which is best interpolation type that can be suited.

Bicubic interpolation (cubic spline) blurs area and because of that destroys edges.
Nearest neighbour interpolation preserves edges, but introduces pixelation.
So best interpolation of bitmap would be hybrid algorithm of those two above - such as 2xSal / Eagle and such.
EDIT: Bicubic interpolation JAVA example code.
Good luck.

Um, this is a horrendously bad idea no matter which way you look at it - you're basically increasing the size of the bitmap by an order of magnitude, but you're not adding any new data - regardless of whether you use a polynomial, linear, or simple copy mechanism the result is going to show either extreme pixelation, extreme blurring, or some mixture of the two.
In general, these algorithms work much better for reducing the size of bitmap images and maintaining overall image integrity, blowing an image up by an order of magnitude is going to be ugly, no matter what you do - the bottom line is that the original information is now dwarfed by the information the interpolater 'made up'

How to check if an image is a scaled version of another image

I am looking for an EASY way to check if an image is a scaled version of another image. It does not have to be very fast, it just should be "fairly" accurate. And written in .NET. And for free.
I know, wishful thinking :-)
I am pretty sure, even without having tried it, that converting the bigger image to the smaller scale and comparing checksums is not working (especially if the smaller version was done with another software then .NET).
The next approach would be to scale down and compare pixels. But first of all, it seems like a really bad idea running a loop over all pixels with a bool comparison results, I am sure there will be some pixels off by a bit or so...
Any library coming to mind? Way back in the university we had some MPEG7 classes, so I am thinking about using a combination of "statistics" like tone distribution, brightness, etc..
Any ideas or links for that topic?
Thanks,
Chris

I think this is going to be your best solution. First check the aspect ratio. Then scale the images to the smaller of the 2 if they're not the same size. Finally, do a hash comparison of the 2 images. This is a lot faster than doing a pixel compare. I found the hash compare method in a post from someone else and just adapted the answer here to fit. I was trying to think of the best way to do this myself for a project where I'm going to have to compare over 5200 images. After I read a few of the posts here I realized I already had everything I needed for it and figured I'd share.
public class CompareImages2
{
public enum CompareResult
{
ciCompareOk,
ciPixelMismatch,
ciAspectMismatch
};
public static CompareResult Compare(Bitmap bmp1, Bitmap bmp2)
{
CompareResult cr = CompareResult.ciCompareOk;
//Test to see if we have the same size of image
if (bmp1.Size.Height / bmp1.Size.Width == bmp2.Size.Height / bmp2.Size.Width)
{
if (bmp1.Size != bmp2.Size)
{
if (bmp1.Size.Height > bmp2.Size.Height)
{
bmp1 = (new Bitmap(bmp1, bmp2.Size));
}
else if (bmp1.Size.Height < bmp2.Size.Height)
{
bmp2 = (new Bitmap(bmp2, bmp1.Size));
}
}
//Convert each image to a byte array
System.Drawing.ImageConverter ic = new System.Drawing.ImageConverter();
byte[] btImage1 = new byte[1];
btImage1 = (byte[])ic.ConvertTo(bmp1, btImage1.GetType());
byte[] btImage2 = new byte[1];
btImage2 = (byte[])ic.ConvertTo(bmp2, btImage2.GetType());
//Compute a hash for each image
SHA256Managed shaM = new SHA256Managed();
byte[] hash1 = shaM.ComputeHash(btImage1);
byte[] hash2 = shaM.ComputeHash(btImage2);
//Compare the hash values
for (int i = 0; i < hash1.Length && i < hash2.Length && cr == CompareResult.ciCompareOk; i++)
{
if (hash1[i] != hash2[i])
cr = CompareResult.ciPixelMismatch;
}
}
else cr = CompareResult.ciAspectMismatch;
return cr;
}
}

One idea to achieve this:
If the image is 10x10, and your original is 40x40
Loop each pixel in the 10x10, then retrieve the 4 pixels representative of that looped pixel.
So for each pixel in the smaller image, find the corresponding scaled amount of pixels in the larger image.
You can then take the average colour of the 4 pixels, and compare with the pixel in the smaller image. You can specify error bounds, IE -10% or +10% bounds are considered a match, others are considered a failure.
Build up a count of matches and failures and use the bounds to determine if it is considered a match or not.
I think this might perform better than scaling the image to the same size and doing a 1pixel:1pixel comparison as I'm not sure how resizing algorithms necesserially work and you might lose some detail which will give less accurate results. Or if there might be different ways and methods of resizing images. But, again I don't know how the resize might work depends on how you go about doing it.

Just scale the larger image back to the size of the smaller one, then compare each pixel by taking the absolute value of the difference in each of the red, green and blue components.
You can then set a threshold for deciding how close you need to be to count it as a match, e.g. if 95%+ of the pixels are within 5% of the colour value, you have a match.
The fuzzy match is necessary because you may have scaling artefacts / anti-aliasing effects.

You'll have to loop over the pixels at some point or another.
Something that is easy to implement yet quite powerful is to calculate the difference between individual color components (RGB) for each pixel, find the average, and see if it crosses a certain threshold. It's certainly not the best method, but for a quick check it should do.

I'd have said roughly what Tom Gullen except I'd just scale down the bigger image to the smaller before comparing (otherwise you're just going to have hard maths if you are comparing a 25x25 with a 30x30 or something).
The other thing I might consider depending on image sizes is to scale them both down to a smaller image. ie if you have one that is 4000x4000 and another that is 3000x3000 then you can scale them both down to 200x200 and compare them at that size.
As others have said you would then need to do a check with a threshold (preferably on colour components) and decide what tolerances work best. I'd suggest this is probably best done by trial and error.

The easiest way is just to scale the biggest image to the smaller images size and compare color difference. Since you don't know if the scaling is cubic or linear (or something else) you have to accept a small difference.
Don't forget to take the absolute value of each pixel difference. ;)

Having absolutely no authority or experience in this area I'm going to make a stab at helping you.
I'd start with the aspect ratio matching by some tolerance, unless you're comparing cropped sections of images, which will makes things a bit harder.
I'd then scan the pixels for regions of similarity, no exactness, again a tolerance level is needed. Then when an area is similar, run along in a straight line comparing one to the other, and find another similarly coloured area. Black & white's gonna be harder.
If you get a hit, you'll have two areas in a line with patches of likeness. With two points you have a reference of length between them and so now you can see what the scaling might be. You could also scale the images first, but this doesn't account for cropped sections where aspects don't match.
Now choose a random point in the source image and get the colour info. Then using the scale factor, find that same random point on the other image and see if the colour checks out. Do it a few times with random points. If many turn up similar it's likely a copy.
You might then want to mark it for further, more CPU intensive, inspection. Either a pixel by pixel comparison or something else.
I know Microsoft (Photosynth) use filters like "outline" (the sort of stuff in Photoshop) to remove the image colours and leave just squrly lines which leave just the 'components' of the picture for matching (they match boundaries and overlap).
For speed, I'd break the problem down into chunks and really think about how humans decide two photos are similar. For non-speed, exhaustively comparing colour will probably get you there.
The process in short:
If you hole punched a sheet of paper randomly 4 times, then put it over two photos, just by seeing the colours coming through you could tell if they were likely a copy and need further inspection.

Convert BitmapImage to grayscale, and keep alpha channel

I'm having an issue with converting a BitmapImage (WPF) to grayscale, whilst keeping the alpha channel. The source image is a PNG.
The MSDN article here works fine, but it removes the alpha channel.
Is there any quick and effective way of converting a BitmapImage to a grayscale?

You should have a look at image transformation using matrices.
In particular, this article describes how to convert a bitmap to grayscale using a ColorMatrix. (It is written in VB.NET, but it should be easy enough to translate to C#).
I haven't tested if it works with the alpha channel, but I'd say it's worth a try, and it definitely is a quick and effective way of modifying bitmaps.

It really depends upon what your source PixelFormat is. Assuming your source is PixelFormats.Bgra32 and that you want to go to grayscale, you might consider using a target pixel format of PixelFormats.Gray16. However, Gray16 doesn't support alpha. It just has 65,535 graduations between black and white, inclusive.
You have a few options. One is to stay with Bgra32 and just set the blue, green and red channels to the same value. That way you can keep the alpha channel. This may be wasteful if you don't require an 8-bit alpha channel (for differing levels of alpha per pixel).
Another option is to use an indexed pixel format such as PixelFormats.Indexed8 and create a palette that contains the gray colours you need and alpha values. If you don't need to blend alpha, you could make the palette colour at position zero be completely transparent (an alpha of zero) and then progress solid black in index 1 through to white in 255.

if relying on API calls fails. You can always try the 'do it yourself' approach: Just get access to the RGBA bytes of the picture, and for every RGBA replace it with MMMA, where M = (R+G+B)/3;
If you want it more perfect, you should add weights to the contribution of the RGB components. I believe your eye is more receptive for green, and as such that value should weigh more.

While not exactly quick and easy, a ShaderEffect would do the job and perform quite well. I've done it myself, and it works great. This article references how to do it and has source associated. I've not used his source, so I can't vouch for it. If you run into problems, ask, and I may be able to post some of my code.
Not every day you get to use HLSL in your LOB app. :)