The aim of this project was 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 imaging and sensing are becoming increasingly important as a core enabler for our data centric society, whether this be for informing advanced technology such as self driving cars, or improved characterization and diagnosis in medicine, or even for fundamental data driven scientific discovery.
There is therefore a need for a new framework for generalized computationally accelerated sensing and processing techniques capable of dealing with the increasingly complex sensing challenges through science and technology. This project aims to 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 time-of-flight cameras.
The overall objectives of the project were to set out a fundamental theoretical framework, 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.
To this end we have made substantial advances to generalize compressed sensing theory to infinite dimensional systems and data driven signal models. We have advanced the field in performance and understanding of compressive algorithms for machine learning and imaging, developing a range of algorithms that significantly reduce computation and data storage.
Most significantly we have developed new theoretical and algorithmic frameworks for learning to image without ground truth data - something that we believe offers the breakthrough capability of true data-driven knowledge discovery in sensing and imaging.
