通过压缩感知改进基于空间域的图像形成
Improving spatial domain based image formation through compressed sensing
摘要：
在本文中，我们改善图像重建在单像素扫描系统中，通过选择一个探测器的最佳视场。图像重建是基于压缩感知和图像质量相比，内插凝视阵列。图像质量比较使用“枯叶”数据集、贝叶斯估计和峰值信噪比（PSNR）测量。压缩感知作为一种插值算法进行了探索，并显示出与Lanczos插值相比具有高概率的改进性能。此外，在单像素扫描系统中的多级采样是通过动态地改变探测器的视野模拟。结果表明，多级采样改善了峰值信噪比的分布。我们进一步探讨了多级采样的期望采样水平分布和PSNR分布。PSNR分布表明，有一个小的水平，这将提高图像质量的插值凝视阵列。我们进一步得出结论，多水平抽样将优于单水平均匀随机抽样的平均。
英文摘要：
In this paper, we improve image reconstruction in a single-pixel scanning system by selecting an detector optimal field of view. Image reconstruction is based on compressed sensing and image quality is compared to interpolated staring arrays. The image quality comparisons use a "dead leaves" data set, Bayesian estimation and the Peak-Signal-to-Noise Ratio (PSNR) measure. Compressed sensing is explored as an interpolation algorithm and shows with high probability an improved performance compared to Lanczos interpolation. Furthermore, multi-level sampling in a single-pixel scanning system is simulated by dynamically altering the detector field of view. It was shown that multi-level sampling improves the distribution of the Peak-Signal-to-Noise Ratio. We further explore the expected sampling level distributions and PSNR distributions for multi-level sampling. The PSNR distribution indicates that there is a small set of levels which will improve image quality over interpolated staring arrays. We further conclude that multi-level sampling will outperform single-level uniform random sampling on average.
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