图像、光场和高维视觉信号压缩捕获的渐进与随机投影
Progressive versus Random Projections for Compressive Capture of Images, Lightfields and Higher Dimensional Visual Signals
摘要：
计算摄影涉及复杂的捕捉方法。一个新的趋势是在低维传感器上捕获诸如视频、多光谱数据和光场的高维视觉信号的投影。精心设计的捕获方法利用变换域中基础信号的稀疏性来减少测量次数并使用适当的重建方法。传统的渐进方法可以使用一系列简单的投影基（如DCT或小波）连续捕获更多的细节，并采用直接的反投影进行重建。随机投影方法不使用任何特定序列，并使用L0最小化进行重建。在本文中，我们分析了自然图像，视频，多光谱数据和光场的统计特性，并比较渐进和随机投影的有效性。我们通过绘制重建SNR对压缩因子来定义有效性。其关键思想是一个程序来衡量最佳情况下的有效性，是快速的，独立于特定的硬件和独立的重建过程。我们相信，这是第一个实证研究，比较不同的有损捕获策略，没有复杂的硬件或重建模糊。范围限于线性非自适应感测。结果表明，随机投影产生显着的优势，比其他投影只有高维信号，并建议更多的研究新生的自适应和非线性投影方法。
英文摘要：
Computational photography involves sophisticated capture methods. A new trend is to capture projection of higher dimensional visual signals such as videos, multi-spectral data and lightfields on lower dimensional sensors. Carefully designed capture methods exploit the sparsity of the underlying signal in a transformed domain to reduce the number of measurements and use an appropriate reconstruction method. Traditional progressive methods may capture successively more detail using a sequence of simple projection basis, such as DCT or wavelets and employ straightforward backprojection for reconstruction. Randomized projection methods do not use any specific sequence and use L0 minimization for reconstruction. In this paper, we analyze the statistical properties of natural images, videos, multi-spectral data and light-fields and compare the effectiveness of progressive and random projections. We define effectiveness by plotting reconstruction SNR against compression factor. The key idea is a procedure to measure best-case effectiveness that is fast, independent of specific hardware and independent of the reconstruction procedure. We believe this is the first empirical study to compare different lossy capture strategies without the complication of hardware or reconstruction ambiguity. The scope is limited to linear non-adaptive sensing. The results show that random projections produce significant advantages over other projections only for higher dimensional signals, and suggest more research to nascent adaptive and non-linear projection methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
