CAREER: A Framework for Sparse Signal Reconstruction for Computer Graphics

职业：稀疏信号重构框架的计算机图形学[j]，电气与计算机工程系。计算机图形学的最新进展在许多方面使我们的社会受益：从娱乐（如电影和游戏）到产品制造（如虚拟原型）和医学（如交互式医学可视化）。然而，尽管有了这些改进，我们离真正的互动式真实感还很远。在这项研究中，研究人员开发了一种新的计算机图形学框架，通过利用新兴的压缩感知领域的思想，提高了现有算法的速度和质量。通过利用现实世界信号的可压缩性，研究人员探索了图像合成和采集的新算法。这项工作的更广泛的影响是，所开发的核心思想不仅将有利于计算机图形学的重要应用，而且还可能影响诸如用于医疗应用的磁共振成像（MRI）等领域。在教育方面，PI通过培养与拉丁美洲的关系，将拉美裔学生纳入研究。这项研究正在为计算机图形学的核心领域：采样和重建开发一种全新的范式。大多数图形算法（例如渲染系统）花费精力对整个信号进行采样，尽管信号之后会被压缩（例如使用JPEG等转换编码压缩算法）。为了充分利用变换域的稀疏性，有效地对信号进行采样，研究人员采用了压缩感知的思想。这就产生了一个框架，它可以通过使用贪婪优化算法从一组稀疏的样本中重建最终图像来加速渲染算法。同样的框架也可以用来加速光传输的获取，这对重照明应用很有用。通过这项工作探索的基础科学将刺激图形社区和相关领域的新研究领域。

CAREER: A Framework for Sparse Signal Reconstruction for Computer GraphicsPradeep Sen, Dept. of Electrical & Computer Engr., University of New MexicoRecent progress in computer graphics has benefited our society in many ways: from entertainment (e.g. movies and games) to product manufacturing (e.g. virtual prototyping) and medicine (e.g. interactive medical visualization). However, despite these improvements we are still far from true interactive photorealism. In this research, the investigators develop a novel framework for computer graphics that improves the speed and quality of existing algorithms by leveraging ideas from the emerging field of compressed sensing. By taking advantage of the compressibility of real-world signals, the researchers explore new algorithms for image synthesis and acquisition. The broader impact of this work is that the core ideas developed will not only benefit important applications in computer graphics, but could also impact areas such as Magnetic Resonance Imaging (MRI) used for medical applications. On the educational side, the PI integrates Hispanics students into the research by fostering relationships with Latin America.This research is developing a fundamentally new paradigm for a core area of computer graphics: sampling and reconstruction. Most graphics algorithms (e.g. rendering systems) expend their effort sampling the entire signal, despite the fact the signal will be compressed afterwards (e.g. with a transform-coding compression algorithm such as JPEG). The investigators apply the ideas of compressed sensing in order to take advantage of the sparsity in the transform domain and sample the signal in an efficient manner. This results in a framework that can be used to accelerate rendering algorithms by reconstructing the final image from a sparse set of samples using greedy optimization algorithms. The same framework can also be used to accelerate the acquisition of light transport which is useful for relighting applications. The fundamental science explored through this work will spur new areas of research within the graphics community and in related fields.