Optimized Sampling Approaches for Compressive Sensing in Multi-Dimensional Datastreams

目前最先进的成像硬件包括将半导体材料非常精确地合成和制造到扩展相机中，现在可以包含多达6400万像素，每个设备的成本可能超过100万英镑。在大多数情况下，这些高灵敏度相机被用于检测非常接近噪声水平的信号，并且作为额外的复杂性，通常寻求表征动态事件（即它们需要能够量化快速移动物体的运动）。这些系统中每个图像帧的数据可以很容易地超过1TB，这意味着相机目前必须在短时间内运行，由于数据传输的延长而延迟响应，并且图像分析可能需要数天/数月/数年的时间来操作和识别数据流中的关键元素。显然，随着全球经济推动更多的自动化和遥感设备的使用，这些限制必须被克服。在最先进的成像系统中，一种可以缓解与速度和精度相关的大量问题的方法是使用压缩感知（CS）方法。在CS方法中，获取图像中的一小部分随机像素并用于重建整个数据集。这立即减少了数据量，并通过使用的子采样量提高了成像速度。这个博士项目的目标是确定用于重建来自卫星、夜视镜和扫描透射电子显微镜等不同来源的图像的子采样水平。通过开发和实现针对图像对比度机制及其分辨率限制的新算法，目标是开发一个连贯的框架，可用于设计具有嵌入式算法的优化成像硬件。

The current state-of-the-art in imaging hardware involves the very precise synthesis and fabrication of semiconducting materials into extended cameras that can now contain up to 64M pixels with a cost that can exceed £1M per device. In most cases, these high sensitivity cameras are implemented to detect signals that are very close to the noise level and as an added complexity are typically looking to characterise dynamic events (i.e. they need to be able to quantify the motion of fast moving objects). The data per image frame in these systems can easily exceed 1TB, meaning that cameras currently have to operate in short bursts, have delayed responses due to the extended transfer of the data, and it can take days/months/years for image analytics to operate and identify key elements in the datastream. Obviously as the global economy pushes towards more automation and the use of remote sensing devices, these limitations have to be overcome.One approach that can alleviate a large number of the problems associated with speed and precision in state-of-the-art imaging systems, is the use of Compressive Sensing (CS) methods. In the CS approach, a small subset of random pixels in the image in acquired and used to reconstruct the full dataset. This immediately reduces the amount of data and increases the imaging speed by the amount of sub-sampling that is used. The goal of this PhD project is to determine the level of sub-sampling that be used to reconstruct images from such diverse sources as satellites, night vision goggles and scanning transmission electron microscopes. By developing and implementing new algorithms for the specifics of the image contrast mechanism and its resolution limits, the goal is to develop a coherent framework that can be used in the design of optimized imaging hardware with embedded algorithms.