Efficient Computational Methods for Robust Multispectral Multiframe Superresolution

鲁棒多光谱多帧超分辨率的高效计算方法

• 批准号: 0429481
• 负责人: Jesse Barlow
• 金额: --
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0429481&HistoricalAwards=false
• 关键词: Efficient; Computational; Methods; Robust; Multispectral

The proposed research considers computational models for multisensor, multiframe superresolution.The underlying mathematical model is a Fredholm integral equation of the first kind. For thesuperresolution problem, not only is the input data contaminated by noise, but the parameters defining the linear operator are uncertain because of uncertainty in sensor alignment. The noisy input makes the problem a candidate for Tikhanov regularized least squares. The uncertainty in the operator make it a candidate for regularized total least squares. Moreover, the uncertainty in the linear operator is structured, thus structured total least squares models are appropriate.This project considers these least squares and total least squares models and appropriate methods forregularizing them. The merits of this activity according to NSF criteria are given below.Intellectual Merit1. The deployment of space variant regularization to the recently proposed least squares and total least squares models.2. The application of total least squares algorithms and space variant regularization to the solutionof blind deconvolution problems using images acquired by multisensor arrays.3. The extension of structured total least squares algorithms to red--green--blue (RGB) color imaging.4. The application of wavelet superresolution algorithms to multisensor array acquired low resolutiondegraded imaging.Broader Impact1. The superresolution problem has impact upon military, commercial, and health careapplications in diverse disciplines such as bioengineering,satellite imaging, and electrical and computer engineering. Other intriguing possibilities include substituting expensive high resolution instruments such asscanning electron microscopes by their cruder, cheaper counterparts and then applying technical methods for increasing the resolution to that derivable with much more costly equipment.2. Survey articles will be prepared to provided tutorial information on our computational methods.This is in addition to publishing the project's results in appropriate refereed journals and conferences.3. The PI and co-PI intend to recruit 4 REUs per year from Penn State's University Scholars program.4. Penn State graduate students will be collaborators with the expectation of producing Ph.D. theses related to the project.

所提出的研究考虑了多传感器、多帧超分辨率的计算模型。基础数学模型是第一类 Fredholm 积分方程。 对于超分辨率问题，不仅输入数据受到噪声污染，而且由于传感器对准的不确定性，定义线性算子的参数也是不确定的。噪声输入使该问题成为 Tikhanov 正则化最小二乘法的候选问题。算子的不确定性使其成为正则化总体最小二乘的候选者。此外，线性算子的不确定性是结构化的，因此结构化总体最小二乘模型是合适的。该项目考虑了这些最小二乘和总体最小二乘模型以及对它们进行正则化的适当方法。根据 NSF 标准，本活动的优点如下。 智力优点 1。将空间变量正则化部署到最近提出的最小二乘和总最小二乘模型中。 2．总体最小二乘算法和空间变量正则化在解决多传感器阵列图像盲反卷积问题中的应用。 3．结构化总体最小二乘算法对红-绿-蓝(RGB)彩色成像的扩展。 4．小波超分辨率算法在多传感器阵列获取低分辨率退化成像中的应用。更广泛的影响1。超分辨率问题对生物工程、卫星成像以及电气和计算机工程等不同学科的军事、商业和医疗保健应用产生影响。其他有趣的可能性包括用更粗糙、更便宜的同类设备替代昂贵的高分辨率仪器（例如扫描电子显微镜），然后应用技术方法来提高使用更昂贵的设备可导出的分辨率。2。将准备调查文章，以提供有关我们的计算方法的教程信息。这是在适当的参考期刊和会议上发布项目结果的补充。3。 PI 和 co-PI 打算每年从宾夕法尼亚州立大学的大学学者计划中招募 4 名 REU。4。宾夕法尼亚州立大学的研究生将成为合作者，期望产出博士学位。与项目相关的论文。