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

本研究的目的是开发一种基于同时稀疏编码的分层表示，并探索其在节能传感中的潜在应用。新构造的分层表示有望提供强大的先验模型，适用于从噪声或不完整样本中进行贝叶斯图像重建。与节能传感相关的两个新应用将被研究：低照度成像和超高清晰度成像，其中所提出的计算方法可以帮助在成本(能量)和获取图像的质量之间取得更好的折衷。这个项目的智能优点包括：1)同时稀疏编码的分层扩展将需要一个新的理论框架，该框架使用最少的人工数学结构(例如，甚至不引入度量的定义)；2)节能传感的这两个应用是计算成像的新前沿，对工业(如消费电子、移动设备)和科学界(如超分辨率显微镜、计算天文学)都具有潜在的高度影响。从更广泛的影响来看，PI将特别努力在更大范围内广泛传播研究成果，以促进稀疏表示和计算成像中可实验重现的研究。该项目将通过在国家科学基金会方案中促进地理和性别多样性，扩大代表性不足群体的参与。这项拟议的研究将以较低的成本将更高质量的图像带到人们的日常生活中，从而使普通公众受益。

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.