Bayesian model selection & calibration for computational imaging

现代数字图像越来越多地通过使用计算机密集成像(CI)技术来生成。事实上，由于现代成像传感器将技术和物理推向了极限，它们产生的原始数据通常是无用的(例如，它们被噪声破坏，只观察到部分或分辨率不足以加速采集过程和降低传感器能量消耗)。成像设备通过使用CI工具分析数据并恢复细节清晰的高质量图像来解决这一难题。在过去的十年里，这一领域取得了重要的进展，大多数CI技术现在都采用了形式化的数学方法来推导解决方案，并研究支撑计算机算法。这导致成像设备速度更快，具有更大的空间分辨率和动态范围，并且对具有挑战性的条件(如夜间、长距离和水下成像)更具健壮性。这反过来通过影响应用领域，如医学成像；天文成像；用于农业、地球科学和国防的卫星和航空遥感；无损测试；以及用于药物和纳米技术开发的显微镜，从而产生显著的社会效益和经济效益。然而，CI解决方案可能对用于分析原始传感器数据的数学模型的选择非常敏感(例如，用于生成图像的数据保真度项和正则化函数)，并且对于所考虑的特定成像设置和场景类型仔细地选择和校准模型是至关重要的。目前，这需要广泛的专家监督，这是昂贵和耗时的。该项目的目的是开发一个新的数学方法和计算机算法工具箱，直接从观测的传感器数据自动选择和校准CI模型，而不使用地面真实数据，并且专家监督最少。这个工具箱将结合来自贝叶斯统计学的先进数学技术和来自随机蒙特卡罗模拟和优化领域的专门计算机算法来开发。预期的结果是，这个工具箱将大大简化CI技术的开发和部署，并因此扩大其在科学和工业中的采用。在该项目期间，建议的工具将应用于与卫星和天文成像有关的两个具有挑战性的CI问题。更准确地说，该项目中开发的方法将用于提高高光谱卫星图像和无线电干涉天文图像的分辨率和细节。这些应用将与穆拉德空间科学实验室、赫里奥特-瓦特大学和图卢兹大学的世界领先专家合作进行研究。这些专家将提供数据、培训和特定应用软件。他们还将帮助传播这项工作并扩大其影响。为了最大限度地提高项目对经济和社会的影响，将在项目网页上公开所有拟议工具的开放源代码，以及文档和教学演示工具包。

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.