Bayesian model selection & calibration for computational imaging

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Modern digital images are increasingly generated by using Computer-intensive Imaging (CI) technology. Indeed, because modern imaging sensors push technology and physics to the limits, the data they produce are generally not useful in their raw form (e.g., they are corrupted by noise and only observed partially or with insufficient resolution to accelerate the acquisition process and reduce sensor energy consumption). Imaging devices address this difficulty by using CI tools to analyse the data and recover high-quality images with fine detail. The last decade has witnessed important advances in this field, with most CI technologies now adopting formal mathematical approaches to derive solutions and to study the underpinning computer algorithms. This has led to imaging devices that are faster, have greater spatial resolution and dynamic range, and are more robust to challenging conditions (e.g., night, long-rage, and underwater imaging). This has in turn produced significant social and economic benefit through impact on application areas such as medical imaging; astronomical imaging; satellite and airborne remote sensing for agriculture, earth sciences and defence; non-destructive testing; and microscopy for drug and nanotechnology development.However, CI solutions can be very sensitive to the choice of the mathematical models used to analyse the raw sensor data (e.g., the data-fidelity term and the regularisation functions used to generate the image), and it is fundamental to carefully select and calibrate models for the specific imaging setup and type of scene considered. Presently, this requires extensive expert supervision, which is expensive and time-consuming.The aim of this project is to develop a toolbox of new mathematical methods and computer algorithms to automatically select and calibrate CI models, directly from the observed sensor data, without using ground truth data, and with minimum expert supervision. This toolbox will be developed by combining advanced mathematical techniques stemming from Bayesian statistics, with specialised computer algorithms from the area of stochastic Monte Carlo simulation and optimisation. The expected outcome is that this toolbox will significantly simplify the development and deployment of CI technology, and amplify its adoption in science and industry as a result.During the project, the proposed tools will be applied to two challenging CI problems related to satellite and astronomical imaging. More precisely, the methods developed in this project will be used to enhance the resolution and fine detail in hyperspectral satellite images and in radio-interferometric astronomical images. These applications will be investigated in collaboration with world-leading experts at the Mullard Space Science Laboratory, Heriot-Watt University, and University of Toulouse. These experts will provide data, training, and application-specific software. They will also help disseminate this work and amplify its impact.To maximise the impact of the project on the economy and society, open-source code for all the proposed tools will be made publicly available on the project webpage, together with documentation and pedagogical demonstration kits.