CAREER: A Framework for Sparse Signal Reconstruction for Computer Graphics

CAREER：A Framework for Sparse Signal Reconstruction for Computer GraphicsPradeep Sen，Department.电气计算机工程师，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（例如电影和游戏）到产品制造（例如虚拟原型）和医学（例如交互式医学可视化）。 然而，尽管有这些改进，我们仍然远离真正的交互式照片写实主义。 在这项研究中，研究人员开发了一种新的计算机图形框架，通过利用新兴的压缩感知领域的思想来提高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。 这项工作的更广泛影响是，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西班牙裔学生融入研究。这项研究正在为计算机图形学的核心领域开发一种全新的范式：采样和重建。大多数图形算法（例如，渲染系统）花费它们的努力对整个信号进行采样，而不管信号将在之后被压缩（例如，利用诸如JPEG的变换编码压缩算法）的事实。 研究人员应用压缩感知的思想，以利用变换域中的稀疏性，并以有效的方式对信号进行采样。 这导致了一个框架，该框架可用于通过使用贪婪优化算法从稀疏样本集重建最终图像来加速渲染算法。 相同的框架也可以用于加速光传输的获取，这对于重新照明应用是有用的。 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.