# Julia Images adaptive histogram equalization vs MATLAB's implementation
**URL:** https://discourse.julialang.org/t/julia-images-adaptive-histogram-equalization-vs-matlabs-implementation/119469
**Category:** Signal and Image Processing
**Tags:** question
**Created:** [September 16, 2024, 5:12pm UTC](https://discourse.julialang.org/t/julia-images-adaptive-histogram-equalization-vs-matlabs-implementation/119469 "2024-09-16T17:12:05Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@cpaniaguam](https://discourse.julialang.org/u/cpaniaguam)
#### Post date: [September 16, 2024, 5:12pm UTC](https://discourse.julialang.org/t/julia-images-adaptive-histogram-equalization-vs-matlabs-implementation/119469/1 "2024-09-16T17:12:05Z")
I’m hoping to get an expert’s opinion on this matter.
In MATLAB:
```matlab
>> img = imread('cameraman.tif');
>> class(img)
ans =
'uint8'
>> sum(img(:))
ans =
7780728
>> img_eq = adapthisteq(img, Range="original", NBins=256, NumTiles=[8 8], ClipLimit=0.01);
>> figure; imshow(cat(2,img,img_eq));
```
The images look like this (original on left):
For the same workflow in Julia I exported `cameraman` from MATLAB to a file to read in (the one in `TestImages` is not the same for some reason) but for the illustration below we’ll stick to the one from `TestImages`
```julia
using Images, ImageContrastAdjustment, TestImages
img = testimage("cameraman")
img = Int64.(img .* 255) # converting to ints as `load` produces eltype Gray{N0f8} and the algo doesn't like that
maxval, minval = maximum(img), minimum(img)
algo = AdaptiveEqualization(minval=minval, maxval=maxval, nbins=256, rblocks=8, cblocks=8, clip=0.01)
img_adjusted = adjust_histogram(img, algo)
Gray.(hcat(img, img_adjusted) / 255) # for the visualization
```
You get this:
As you can see the image is exceedingly oversharpened. Is there something I am overlooking? I noticed the default values of `(minval, maxval)` are set to `(0, 1)` which seem low for uint8 images. However, I converted the input image to gray with `Gray.(img / 255)` using those default values and got the same effect still.
---
### Author:  [@zygmuntszpak](https://discourse.julialang.org/u/zygmuntszpak)
#### Post date: [September 21, 2024, 1:04pm UTC](https://discourse.julialang.org/t/julia-images-adaptive-histogram-equalization-vs-matlabs-implementation/119469/2 "2024-09-21T13:04:55Z")
> [@cpaniaguam](#):
>
> `img = testimage("cameraman")`
It has been a while since I implemented this, so I don’t immediately recall all of the details. I’m not sure what how MATLAB has interpreted the parameters, but it looks like my parameter `clip` and MATLABs `ClipLimit` are not exactly the same. At first glance, it looks like `clip ≈ 1 - ClipLimit`. So if you try
```julia
using Images, ImageContrastAdjustment, TestImages
img = testimage("cameraman")
algo = AdaptiveEqualization(minval=minval, maxval=maxval, nbins=256, rblocks=8, cblocks=8, clip=0.99)
img_adjusted = adjust_histogram(img, algo)
```
you should get something similar to your MATLAB result.
You don’t need to convert the image to Int64. The algorithm will work on the `Gray{N0f8}` input. All images are represented on a [0…1] intensity scale, so 0 is black and 1 is white. The rationale for this representation is explained in the docmentation: [Quickstart · JuliaImages](https://juliaimages.org/latest/tutorials/quickstart/)[JuliaImages Quickstart](https://juliaimages.org/latest/tutorials/quickstart/)