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.

目前最先进的成像硬件涉及将半导体材料非常精确地合成和制造成扩展相机，这些相机现在可以包含多达 6400 万像素，而在大多数情况下，每台设备的成本可能超过 100 万英镑。灵敏度相机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，通常希望表征动态事件（即，它们需要能够量化快速移动物体的运动）中每个图像帧的数据。这些系统很容易超过1TB，意味着相机目前必须在短时间内运行，由于数据传输时间较长，响应会有所延迟，并且图像分析可能需要数天/数月/数年才能运行并识别数据流中的关键元素。全球经济推动更多的自动化和遥感设备的使用，必须克服这些限制。一种可以缓解与最先进的成像系统的速度和精度相关的大量问题的方法是使用压缩感知 (CS)在 CS 方法中，获取图像中的一小部分随机像素并用于重建完整数据集，这会立即减少数据量并通过使用的子采样目标提高成像速度。该博士项目的目的是通过开发和实施针对图像对比度机制和细节的新算法，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。其分辨率限制，目标是开发一个一致的框架，可用于设计具有嵌入式算法的优化成像硬件。