EPFL Research Visit

洛桑联邦理工学院研究访问

• 批准号: EP/K032275/1
• 负责人: Mike Davies
• 金额: $ 3.68万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FK032275%2F1
• 关键词: EPFL; Research; Visit

This proposal is for a research visit to share research ideas in sparse representations and compressed sensing between the signal processing labs in EPFL and the Edinburgh Compressed Sensing research group. Sparse representations have emerged as a very powerful technique for describing data in signal and image processing, and increasingly in many areas of machine learning. The underlying structure that they expose helps make challenging tasks such as detection, classification, separation and signal acquisition tractable and aids computational efficiency. Compressed sensing is a subfield of sparse approximation that involves a radical re-thinking of the sampling process and enables their acquisition (sensing) through many fewer samples than would be predicted by the traditional Nyquist criterion. It has generated a wealth of interest in recent years, not just within the signal processing community but across many related disciplines and applications: from seismology and radar to genomic sequencing. In this project we will explore the next generation of sparse representaions and compressed sensing schemes that combine advanced imaging modalities with novel structured signal models.

该提议是为了在EPFL的信号处理实验室和爱丁堡压缩感知研究组之间进行研究访问，以分享稀疏表示和压缩感知的研究思路。稀疏表示已经成为一种非常强大的技术，用于描述信号和图像处理中的数据，并且越来越多地用于机器学习的许多领域。它们所揭示的底层结构有助于使诸如检测、分类、分离和信号采集等具有挑战性的任务变得易于处理，并有助于提高计算效率。压缩感知是稀疏近似的一个子领域，它涉及对采样过程的彻底重新思考，并通过比传统奈奎斯特准则预测的少得多的样本来实现它们的采集（感知）。近年来，它不仅在信号处理界，而且在许多相关学科和应用中引起了广泛的兴趣：从地震学和雷达到基因组测序。在这个项目中，我们将探索下一代的稀疏表示和压缩传感方案，结合联合收割机先进的成像方式与新颖的结构化信号模型。

项目成果

Recipes for stable linear embeddings from Hilbert spaces to R^m

从希尔伯特空间到 R^m 的稳定线性嵌入的秘诀

• DOI: 10.48550/arxiv.1509.06947
• 发表时间: 2015
• 期刊: arXiv e-prints
• 作者: Puy Gilles

Dictionary Learning for Fast Classification Based on Soft-thresholding

• DOI: 10.1007/s11263-014-0784-7
• 发表时间: 2014-02
• 期刊: International Journal of Computer Vision
• 影响因子: 19.5
• 作者: Alhussein Fawzi;M. Davies;P. Frossard

A Compressed Sensing Framework for Magnetic Resonance Fingerprinting

• DOI: 10.1137/130947246
• 发表时间: 2014-01-01
• 期刊: SIAM JOURNAL ON IMAGING SCIENCES
• 影响因子: 2.1
• 作者: Davies, Mike;Puy, Gilles;Wiaux, Yves
• 通讯作者: Wiaux, Yves