CCSS: Simultaneous Sparse Coding for Energy Efficient Sensing: from Low-illumination to Super-Clarity Imaging

CCSS：用于节能传感的同步稀疏编码：从低照度到超清晰成像

基本信息

批准号：
1305661
负责人：
Xin Li
金额：
$ 24.94万
依托单位：
West Virginia University Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2013
资助国家：
美国
起止时间：
2013-07-15 至 2017-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1305661&HistoricalAwards=false
关键词：
CCSS Simultaneous Sparse Coding Energy

项目摘要

The objective of this research is to develop a hierarchical representation based on simultaneous sparse coding and explore its potential applications in energy-efficient sensing. The newly constructed hierarchical representation is expected to offer powerful prior models suitable for Bayesian reconstruction of images from noisy or incomplete samples. Two novel applications related to energy-efficient sensing will be studied: low-illumination imaging and super-clarity imaging where the proposed computational approach can help strike a better tradeoff between the cost (energy) and the quality of acquired images. The intellectual merit of this project includes: 1) hierarchical extension of simultaneous sparse coding will require a new theoretical framework operating with the fewest artificial mathematical structures (e.g., not even the definition of a metric is introduced); 2) the two applications of energy-efficient sensing are new frontiers of computational imaging with potential high impact on both industry (e.g., consumer electronics, mobile devices) and scientific community (e.g., super-resolution microscopy, computational astronomy).With respect to broader impacts, the PI will make special effort to broadly disseminate the research results at a large scale to promote experimentally-reproducible research in sparse representations and computational imaging. This project will broaden the participation of underrepresented groups by promoting geographic and gender diversities within the NSF program. The proposed research is beneficial to the general public by bringing images of higher quality to people's daily lives at a lower cost.

这项研究的目的是基于同时稀疏编码的层次结构表示，并探索其在节能感测中的潜在应用。新构建的层次表示形式有望提供有力的先前模型，适用于噪音或不完整样本中图像的贝叶斯重建。将研究与节能感测相关的两种新型应用：低弹片成像和超级效果成像，其中建议的计算方法可以帮助在成本（能量）和获得图像的质量之间进行更好的折衷。该项目的智力优点包括：1）同时稀疏编码的层次结构扩展将需要一个新的理论框架，并具有最少的人工数学结构（例如，甚至引入了度量的定义）； 2）能节能感测的两个应用是计算成像的新领域，对行业（例如，消费电子，移动设备）和科学界（例如，超分辨率显微镜，计算天文学）的潜在影响很高成像。该项目将通过促进NSF计划中的地理和性别多样性来扩大代表性不足的群体的参与。拟议的研究通过将较高质量的图像带入人们的日常生活，对公众有益。