Python implementation of general midway equalization using arbitrary number of grayscale images, .
Example runs using grayscale images avaliable in the following jypitor notebook
general_midway_equalization.ipynb
It is a requirement that all images have same dimensions.
Let denotes the normalized culumative histogram of a grayscale image,
, and
the pseudo-inverse of
.
The midway equalization method can be generalized to N arbitrary number of images. Specifically,
where for
and
.
I refer to utils/functions.py for detailed implementation.
For further describtions and alternative methods please see [1] and [2].
- Step 1: Open
general_midway_equalization.ipynb. - Step 2: Load N number grayscale images and make sure they all have same dimension. Stack them on top of each others either in a list or array.
- Step 3: Run codes from
utils/functions.pyand plot results.
Try it out yourself, and if you have any questions don't hesitates to create an issue. Cheers!
[1] Julie Delon. Midway Image Equalization. In: "Journal of Mathematical Imaging and Vision", Springer Verlag, 21 (2) (2004), pp.119-134. DOI: 10.1023/B:JMIV.0000035178.72139.2d
[2] Thierry Guillemot and Julie Delon. Implementation of the Midway Image Equalization. In: "Image Processing On Line", 6 (2016), pp. 114-129. DOI: 10.5201/ipol.2016.140