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javaimage-processingimagej

ImageJ: stretchHistogram vs equalize

I am working with on a project where I have to display some pictures (grayscale) and I notice that many of them were too dark to properly see.

Then looking at ImageJ API documentation, I found the class: ij.plugin.ContrastEnhancer

And there is two methods there that I am having a hard time to understand their conceptual differences stretchHistogram() and equalize() both make the image brighter, but I still want to understand the differences.

My question is: what is the conceptual differences between those methods?

Solution

  • A histogram stretch is where you have an image that has a low dynamic range - so all of the pixel intensities are concentrated in a smaller band than the 0 to 255 range of an 8-bit greyscale image, for example. So the darkest pixel in the image may be 84 and the brightest 153. Stretching just takes this narrow range and performs a linear mapping to the full 0 to 255 range. Something like this:

    linear

    Histogram equalisation attempts to achieve a flat histogram - so all possible pixel intensities are equally represented in the image. This means that where there are peaks in the histogram - concentrations of values in a certain range - these are expanded to cover a wider range so that the peak is flattened, and where there are troughs in the histogram, these are mapped to a narrower range so that the trough is levelled out. Again, something like this:

    equalised

    For a uni-modal histogram with a low dynamic range, the two operations are roughly equivalent, but in cases where the histogram already covers the full range of intensities the histogram equalisation gives a useful visual improvement while stretching does nothing (because there's nothing to stretch). The curve for mapping to equalise a histogram is derived from the cumulative distribution (so imagine each histogram bar is the sum of all previous values) and theoretically it's possible to achieve a perfectly flat histogram. However, because we are (normally) dealing with discrete values of pixel intensities, histogram equalisation gives an approximation to a flat histogram as shown above.

    Note that the images above were taken from this web page.