Optimising Big Images

  • Tuomo Valkonen
Part of the Studies in Big Data book series (SBD, volume 18)


We take a look at big data challenges in image processing. Real-life photographs and other images, such ones from medical imaging modalities, consist of tens of million data points. Mathematically based models for their improvement—due to noise, camera shake, physical and technical limitations, etc.—are moreover often highly non-smooth and increasingly often non-convex. This creates significant optimisation challenges for the application of the models in quasi-real-time software packages, as opposed to more ad hoc approaches whose reliability is not as easily proven as that of mathematically based variational models. After introducing a general framework for mathematical image processing, we take a look at the current state-of-the-art in optimisation methods for solving such problems, and discuss future possibilities and challenges.


Graphic Processing Unit Augmented Lagrangian Method Imaging Problem Convex Relaxation Total Variation Regularisation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The preparation of this chapter was supported by a Prometeo fellowship of the Senescyt (Ecuadorian Ministry of Education, Science, Technology, and Innovation) while the author was at the Centre for Mathematical Modelling (ModeMat), Escuela Politénica, Nacional, Quito, Ecuador.


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© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Applied Mathematics and Theoretical PhysicsUniversity of CambrigeCambrigeUK

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