论文标题
通过压缩传感改善基于空间域的图像形成
Improving spatial domain based image formation through compressed sensing
论文作者
论文摘要
在本文中，我们通过选择检测器最佳视野来改善单像素扫描系统中的图像重建。图像重建基于压缩感测和图像质量与插值凝视阵列进行了比较。图像质量比较使用“死叶”数据集，贝叶斯估计和峰值信号与噪声比率（PSNR）度量。 与兰开斯插值相比，压缩传感被探索为一种插值算法，并显示出高概率的性能。此外，通过动态更改检测器视野来模拟单像素扫描系统中的多级采样。结果表明，多级采样可改善峰信号与噪声比的分布。 我们进一步探索用于多级抽样的预期采样级分布和PSNR分布。 PSNR分布表明存在一小部分水平，可以改善与插值凝视阵列相比的图像质量。我们进一步得出结论，多级采样将平均胜过单级均匀的随机抽样。
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.
