Optimized Sampling Approaches for Compressive Sensing in Multi-Dimensional Datastreams

多维数据流中压缩感知的优化采样方法

• 批准号: 2599531
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2599531
• 关键词: Optimized; Sampling; Approaches; Compressive; Sensing

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.

目前成像硬件的最新技术涉及将半导体材料非常精确地合成和制造成扩展相机，现在可以包含高达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，这些高灵敏度摄像机被实现为检测非常接近噪声水平的信号，并且作为额外的复杂性，它们通常寻找动态事件（即，它们需要能够量化快速移动物体的运动）。这些系统中每帧图像的数据很容易超过1 TB，这意味着相机目前必须在短时间内运行，由于数据传输时间延长而延迟响应，图像分析可能需要数天/数月/数年的时间才能运行并识别数据流中的关键元素。显然，随着全球经济向更自动化和遥感设备的使用的推动，这些限制必须被克服。一种可以缓解与最先进的成像系统中的速度和精度相关的大量问题的方法是使用压缩传感（CS）方法。在CS方法中，获取图像中随机像素的一个小子集，并用于重建完整的数据集。这立即减少了数据量，并通过所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从卫星，夜视镜和扫描透射电子显微镜等不同来源重建图像的子采样水平。通过开发和实施新的算法的图像对比度机制和其分辨率限制的细节，我们的目标是开发一个连贯的框架，可用于设计的优化成像硬件与嵌入式算法。