Report

Teaser, summary, work performed and final results

Periodic Reporting for period 2 - C-SENSE (Exploiting low dimensional models in sensing, computation and signal processing)

Teaser

The aim of this project is to develop the next generation of compressive and computational sensing and processing techniques. The ability to identify and exploit good signal representations is pivotal in many signal and data processing tasks. During the last decade sparse...

Summary

The aim of this project is to develop the next generation of compressive and computational sensing and processing techniques.

The ability to identify and exploit good signal representations is pivotal in many signal and data processing tasks. During the last decade sparse representations have provided stunning performance gains for applications such as: imaging coding, computer vision, super-resolution microscopy and most recently in MRI, achieving many-fold acceleration through compressed sensing (CS). However, iterative reconstruction techniques are often not adopted in commercial imaging/sensing systems as they typically incur at least an order of magnitude more computation than traditional techniques.

Today there is a need for a new framework for generalized computationally accelerated sensing and processing techniques to deal with the increasingly complex sensing and data challenges as well as the ever-growing demands of the user. The project should enable us to tackle a new generation of data-driven, physics-aware and task-orientated sensing systems in application domains such as advanced radar, CT and MR imaging and emerging sensing modalities such as multi-spectral time-of-flight cameras.

The overall objectives of the project are to develop and analyse new algorithms, signal models and data processing tools, accommodating everything from physical laws to data-driven models and neural networks, exploiting underlying low dimensional structure to reduce computation and sensing costs, as well as enhance performance.

Work performed

To date the project has developed new theory and algorithms for signal processing, optimisation and advanced image reconstruction. In each case the focus has been the role of underlying low-dimensional structure within the data that can potentially be exploited for computation or performance gains.
Applications of these ideas have also be pursued in advanced CT and MRI medical imaging to provide accelerated imaging and improved functionality.
In machine learning the ideas are also being applied to significantly reduce the amount of data required to solve certain learning problems.

Final results

The following progress beyond the state of the art has so far been achieved:
• Extending compressed sensing theory to infinite dimensional problems
• Accelerated iterative reconstruction algorithms that exploit inexact computations
• A quantitative CT reconstruction algorithm that can predict electron or mass density from a standard single polyenergetic X-ray source.