mclahe 1.1.0
Tensorflow implementation of the Multidimensional Contrast Limited Adaptive Histogram Equalization (MCLAHE) procedure
Requires Python: >=3.8

# Multidimensional Contrast Limited Adaptive Histogram Equalization

## Note: for the latest version of the software please visit [VincentStimper/mclahe](https://github.com/VincentStimper/mclahe).

## Introduction

Multidimensional Contrast Limited Adaptive Histogram Equalization (MCLAHE) is a multidimensional extension of the
contrast enhancement procedure CLAHE for images. It can be applied to datasets with an arbitrary number of dimensions.
This repository comprises an implementation in Tensorflow and one in NumPy only. Both can be run on multiple CPUs, and
the Tensorflow implementation works with other hardware accelerators such as GPUs as well.


## Installation

The Tensorflow implementation of the package can be installed via pip

```
pip install --upgrade https://github.com/mpes-kit/mclahe/archive/master.zip
```

To install the NumPy version, run

```
pip install --upgrade https://github.com/VincentStimper/mclahe/archive/numpy.zip
```

### Requirements

The main package requires `numpy` and `tensorflow`. `tensorflow` needs to be installed manually depending on the
hardware in use. The package supports `tensorflow` 2.0 and later versions.
A comprehensive installation guide is given at the [Tensorflow webpage](https://www.tensorflow.org/install).
For the NumPy version, only `numpy` needs to be installed. The example notebook requires `matplotlib` in addition. 


## Sample datasets

### Fluorescence microscopy

Fluorescence microscopy can be used to capture time resolved volumetric images of a developing embryo. To illustrate the
effectiveness of MCLAHE we applied it to a dataset of an organism of species phallusia mammillata (available
[here](http://bioemergences.iscpif.fr/bioemergences/openworkflow-datasets.php)). To reduce the noise in the data, we
preprocessed it by a median filter with kernel size (2, 2, 2, 1) in the (x, y, z, t) space 

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;![MCLAHE applied to fluorescence microscopy data](https://github.com/VincentStimper/mclahe/blob/master/images/demo_fm.gif "MCLAHE applied to fluorescence microscopy data")

The above image show a slice along the z-axis through the data. The unit hpf means hours post fertilization. Our
hyperparameters of choice were the kernel size (20, 20, 10, 25), 256 bins in the histogram, and a clipping limit of
0.25. We used a global histogram range


### Multidimensional photoemission spectroscopy

Multidimensional photoemission spectroscopy is a technique to map the electronic band structure in a 4D space consisting
of two momentum (k<sub>x</sub>, k<sub>y</sub>), an energy (E), and a pump-probe time delay (t<sub>pp</sub>) coordinate. In the raw data, the excited
state (E > 0) is not visible. 4D CLAHE makes is visible while enhancing local features in the other states but preserving
the temporal intensity changes.

![MCLAHE applied to MPES data](https://github.com/VincentStimper/mclahe/blob/master/images/demo_mpes.gif "MCLAHE applied to MPES data")

Here, we used a kernel size of (30, 30, 15, 20) in (k<sub>x</sub>, k<sub>y</sub>, E, t<sub>pp</sub>) space, 256 histogram bins, and
a clipping limit of 0.02. We made use of the adaptive histogram range for processing this dataset.


## Documentation

```python
def mclahe(x, kernel_size=None, n_bins=128, clip_limit=0.01, adaptive_hist_range=False, use_gpu=True):
    ...
```

### Parameters

* `x`: Input data as a numpy array with a arbitrary number of dimensions
* `kernel_size`: Tuple, list, or numpy array specifying the kernel size along the data dimensions. If `kernel_size=None`,
the kernel size is set to 1/8 of the data size along each dimension. This is a typical choice for photographs. For more
complex dataset, the kernel size should be roughly of the size of the features which shall be enhanced by MCLAHE.
* `n_bins`: Integer specifying the number of histogram bins used within each kernel. Typically, it is set to a power of
two like 128 or 256 but any number could be chosen.
* `clip_limit`: Float being the share of voxels within a kernel at which the histogram shall be clipped. A clipping
limit of 1 corresponds to standard histogram equalization and if it is smaller than 1 the contrast enhancement is
limited. Since a relative histogram height of `1/n_bins` corresponds to a uniform distribution the clipping limit should
be higher than that.
* `adaptive_hist_range`: flag saying whether an adaptive histogram range (AHR) shall be used or not. With AHR, each
histogram uses its own range determined by the minimum and maximum intensity in the kernel. Otherwise, the range is set
by the global minimum and maximum within the data.
* `use_gpu`: flag specifying whether a GPU shall be used for computations if available. If there is no GPU available,
this flag has no influence. If the dataset is very large it might be necessary to use CPUs only to not run out of memory
on the GPU.


## Citation

If you are using this package within your own projects, please cite it as
> V. Stimper, S. Bauer, R. Ernstorfer, B. Schölkopf and R. P. Xian, "Multidimensional Contrast Limited Adaptive Histogram Equalization," in IEEE Access, vol. 7, pp. 165437-165447, 2019.

Bibtex code
```
@article{Stimper2019,
    author={V. {Stimper} and S. {Bauer} and R. {Ernstorfer} and B. {Schölkopf} and R. P. {Xian}},
    journal={IEEE Access},
    title={Multidimensional Contrast Limited Adaptive Histogram Equalization},
    year={2019},
    volume={7}, 
    pages={165437-165447},
    doi={10.1109/ACCESS.2019.2952899},
}
```