Software & Code

Orasis Image Enhancement Software: Bridging the gap between what you see and what the camera captures


Orasis is a biologically-inspired, image enhancement software, which employs the characteristics of the ganglion cells of the Human Visual System. Many times the image captured by a camera and the image in our eyes are dramatically different. Especially when there are shadows or highlights in the same scene. In these cases our eyes can distinguish many more details in the shadows or highlights, while the image captured by the camera suffers from loss of visual information in these regions.

Orasis attempts to bridge the gap between "what you see" and "what the camera outputs". It enhances the shadow or the highlight regions of an image, while keeping intact all the correctly exposed ones. The final result is a lot closer to the human perception of the scene, than the original captured image, revealing visual information that otherwise wouldn't be available to the human observer. Additionally to the above, Orasis can correct low local contrast, colors and noise.

The following video demonstrates the use of the Orasis desktop application. It is developed in Visual Studio C# and is available for Windows platforms. 








Download Orasis 1.2 (for windows)

              • JPEG compression support
  • EXIF data support
  • Keyboard shortcuts
  • Decorrelation between brightness and local contrat enhancement
  • Automatic Color Saturation
  • Automatic check for Updates
  • Improved memory management
  • Improved Noise reduction algorithms
  • Improved Automatic selection of parameters
  • Improved Color Correction algorithm
  • Improved Independent RGB mode
  • Improved interface
  • HDR exposure fusion


Here are some screenshots from the user interface of Orasis.







Orasis for iOS 

Orasis algorithm is available for iPhone & iPad. Click here for visiting the iTunes application page, or check out the official Orasis iOS website: http://orasis-imaging.com
Orasis was among the 5 innovative winner applications, distinguished during the 2011 Velti mobile demo day: http://demoday.velti.com/winners


Check out the following video, demonstrating the use of the Orasis iOS application.

Orasis iOS demo





















Free MATLAB code for HDR Multi-Exposure Image Fusion

Matlab code for this project is freely available here from Matlab File Exchange. Please feel free to download it and try it out! I would appreciate any feedback :)

























Free PYTHON / MATLAB code for dataset shaping and balancing

Python code and Matlab code related to the paper "Shaping Datasets: Optimal Data Selection for Specific Target Distributions", which will be presented in ICIP2016. 

It includes:
  1. Scripts for replicating the results
  2. A generic function that can be used for shaping (or balancing) datasets
If you use this code in your research, please cite the following papers:
Vonikakis, V., Subramanian, R., Arnfred, J., & Winkler, S. A Probabilistic Approach to People-Centric Photo Selection and Sequencing. IEEE Transactions in Multimedia, 11(19), pp.2609-2624, 2017.
V. Vonikakis, R. Subramanian, S. Winkler. (2016). Shaping Datasets: Optimal Data Selection for Specific Target DistributionsProc. ICIP2016, Phoenix, USA, Sept. 25-28.

Shaping Datasets






Automatic Slideshow Creation / Image Appeal Measure / Image Selection Probabilities (from crowdsourcing)

This is the supplementary material for the paper: "A Probabilistic Approach to People-Centric Image Selection and Sequencing"

It includes:
  1. The image selection probabilities for 13 image/face attributes, learnt through a large-scale crowdsourcing study.
  2. A MATLAB implementation of the Mixed-Integer Linear Programming (MILP) technique for dataset shaping (or balancing), including minimization of the cross-dimensional correlations. 
  3. A MATLAB implementation of the Integer Linear Programming (ILP) technique for automatic appealing slideshow creation, based on the learnt selection probabilities from the crowd.  
The code can be downloaded from here

A Probabilistic Approach to People-Centric Image Selection and Sequencing