Can any one share the code for multiscale retinex algorithm for image enhancement? Multiscale retinex. Image Processing On Line, 71-88. The first one use an exponential downscaling by 'scalefactor' until 'scalefactor^nscale', this has the advantage of speed up the algorithm for large image but produces more halo artifacts. The second one take as input the different scales disered and thus allow non constrain scaling.
A simple command-line interface for image enhancement and color balancing.
This application is being developed mainly to play with a few algorithms that I like. I am aiming at making the implementations work for 2D, 3D and 4D images. Currently implemented algorithms are:
- Multi-scale retinex [1, 3]
- Simplest color balance [2]
- Simplex color balance (based on [3, 4], not published)
Dependencies
Python 3 and the following packages:
Package | Tested version |
---|---|
OpenCV | 3.4.4 |
SciPy | 1.2.0 |
NiBabel | 2.2.1 |
NumPy | 1.15.4 |
Installation
Clone this repository or download the latest release. In your command line, change directory to folder of this package and run the following:
If everything went fine, typing
iphigen -h
or iphigen_nifti -h
in the command-line should show the help menu now.Usage
Retinex with intensity balance
Color balance
Retinex with simplest color balance
Use with Nifti files
Use within python scripts
See script examples here.
Please use github issues to report bugs or make suggestions.
The project is licensed under BSD-3-Clause.
This application is based on the following work:
- Jobson, D. J., Rahman, Z. U., & Woodell, G. A. (1997). A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image Processing, 6(7), 965–976. http://doi.org/10.1109/83.597272
- Limare, N., Lisani, J., Morel, J., Petro, A. B., & Sbert, C. (2011). Simplest Color Balance. Image Processing On Line, 1(1), 125–133. http://doi.org/10.5201/ipol.2011.llmps-scb
- Petro, A. B., Sbert, C., & Morel, J. (2014). Multiscale Retinex. Image Processing On Line, 4, 71–88. http://doi.org/10.5201/ipol.2014.107
- Gulban, O. F. (2018). The Relation between Color Spaces and Compositional Data Analysis Demonstrated with Magnetic Resonance Image Processing Applications. Austrian Journal of Statistics, 47(5), 34–46. http://doi.org/10.17713/ajs.v47i5.743