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首页> 外文期刊>IEEE Signal Processing Magazine >Enhanced Compressive Imaging Using Model-Based Acquisition: Smarter sampling by incorporating domain knowledge
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Enhanced Compressive Imaging Using Model-Based Acquisition: Smarter sampling by incorporating domain knowledge

机译：使用基于模型的采集增强的压缩成像：通过合并领域知识来进行更智能的采样

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摘要

Compressive imaging (CI) is a subset of computational photography where a scene is captured via a series of optical, transform-based modulations before being recorded at the detector. However, unlike previous transform imagers, compressive sensors take advantage of the inherent sparsity in the image and use specialized algorithms to reconstruct a high-resolution image with far lower than 100% of the total measurements. Initial CI systems exploited the properties of random matrices used in other areas of compressive sensing (CS); however, in the case of imaging, there are immense benefits to be derived by designing measurement matrices that optimize specific objectives and enable novel capabilities. In this article, we survey recent results on measurement matrix designs that provide the ability of real-time previews, signature-selective imaging, and reconstruction-free inference.
机译：压缩成像（CI）是计算摄影的子集，其中场景通过一系列基于变换的光学调制被捕获，然后在检测器上进行记录。但是，与以前的变换成像器不同，压缩传感器利用了图像中固有的稀疏性，并使用专门的算法来重建高分辨率图像，而其分辨率远低于总测量值的100％。最初的CI系统利用了在压缩感知（CS）其他领域中使用的随机矩阵的特性；但是，在成像的情况下，通过设计可优化特定目标并实现新颖功能的测量矩阵可以带来巨大的好处。在本文中，我们调查了有关测量矩阵设计的最新结果，这些设计提供了实时预览，签名选择性成像和无重构推断的功能。

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