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image processing algorithms at University of Regensburg (short: iPAUR)
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(image Processing Algorithms at University of Regensburg)

The code is implemented for my master thesis found in the src/LaTex folder.

Main Publications

A list of the main publications I follow in my thesis and coding:

Chambolle Antonin and Pock Thomas. A first-order primal-dual algorithm for convex problems with applications to imaging. ”Journal of Mathematical Imaging and Vision”, 40(1):120–145, 2011.

A. Chambolle, V. Caselles, D. Cremers, M. Novaga, and T. Pock. An introduction to total variation for image analysis. In ”Theoretical Foundations and Numerical Methods for Sparse Recovery”. De Gruyter, 2010.

T. Pock, D. Cremers, H. Bischof, and A. Chambolle. An algorithm for minimizing the piecewise smooth mumford-shah functional. In ”IEEE International Conference on Computer Vision (ICCV)”, Kyoto, Japan, 2009.

E. Strekalovskiy and D. Cremers. Real-time minimization of the piecewise smooth mumford-shah functional. In ”European Conference on Computer Vision (ECCV)”, pages 127–141, 2014.

Image References

Implemented Algorithms and Basic Image Processing

  • Serial algorithms:
    • ROF-Model
    • TV-L1-Model
    • Image Inpainting
    • Huber-ROF-Model
    • Real-Time Minimizer for the Mumford-Shah functional
  • Other basic serial algorithms:
    • mean value blur
    • gaussian blur
    • canny edge detection
    • dilatation
    • erosion
    • duto filter
    • gradient filter (Sobel, Prewitt, Robert's Cross)
    • color space conversions
    • inverse image
    • laplace filter (operator)
    • median filter
  • Parallel algorithms:
    • Primal-Dual Algorithm to solve the convex relaxed Mumford-Shah functional using Dykstra's projection algorithm
    • Primal-Dual Algorithm to solve the convex relaxed Mumford-Shah functional using an approach with Lagrange multipliers



For the use of the GPU implementation, CUDA is needed.


For reading and writing images OpenCV is needed.

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