Project description
New algorithms to handle large amounts of image-processing data
Several scientific fields such as biology, medicine, physics, and chemistry produce an increasingly large amount of data that must be efficiently processed and stored. Sifting through this deluge of data requires the design of new algorithms that are computationally scalable, robust and versatile. The majoration-minimization (MM) framework, which employs a class of efficient and effective optimisation algorithms, plays a crucial role in efficient data-processing. The EU-funded MAJORIS project will design new MM algorithms that can remain efficient when dealing with big data. Work will include the development of acceleration strategies and convergence analysis. The targeted applications are high-resolution multiphoton microscopy, breast tomosynthesis image reconstruction, and mass spectrometry data-processing.
Objective
Mathematical optimization is the key to solving many problems in science, based on the observation that physical systems obey a general principle of least action. While some problems can be solved analytically, many more can only be solved via numerical algorithms. Research in this domain has proved essential over many years. In addition, science in general is changing. Increasingly, in biology, medicine, astronomy, chemistry, physics, large amounts of data are collected by constantly improving signal and image acquisition devices, that must be analyzed by sophisticated optimization tools. In this proposal, we consider handling optimization problems with large datasets. This means minimizing a cost function with a complex structure and many variables. The computational load for solving these problems is too great for even state-of-the-art algorithms. Thus, only relatively rudimentary data processing techniques are employed, reducing the quality of the results and limiting the outcomes that can be achieved via these novel instruments. New algorithms must be designed with computational scalability, robustness and versatility in mind.
In this context, Majorization-Minimization (MM) approaches have a crucial role to play. They consist of a class of efficient and effective optimization algorithms that benefit from solid theoretical foundations. The MAJORIS project aims at proposing a breakthrough in MM algorithms, so that they remain efficient when dealing with big data. I propose to tackle several challenging questions concerning algorithm design. These include acceleration strategies, convergence analysis with complex costs and inexact schemes. I will also tackle practical, massively parallel and distributed architecture implementations. Three specific applications are targeted: super-resolution in multiphoton microscopy in biology; on-the-fly reconstruction for 3D breast tomosynthesis in medical imaging; and mass spectrometry data processing in chemistry.
Fields of science
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencesphysical sciencesopticsmicroscopysuper resolution microscopy
- natural sciencesphysical sciencesastronomy
- natural scienceschemical sciencesanalytical chemistrymass spectrometry
- natural sciencescomputer and information sciencesdata sciencedata processing
Programme(s)
Topic(s)
Funding Scheme
ERC-STG - Starting GrantHost institution
78153 Le Chesnay Cedex
France