Hu Xuemei, Suo Jinli, Yue Tao, Bian Liheng, Dai Qionghai
Opt Express. 2015 May 4;23(9):11092-104. doi: 10.1364/OE.23.011092.
Ghost imaging has rapidly developed for about two decades and attracted wide attention from different research fields. However, the practical applications of ghost imaging are still largely limited, by its low reconstruction quality and large required measurements. Inspired by the fact that the natural image patches usually exhibit simple structures, and these structures share common primitives, we propose a patch-primitive driven reconstruction approach to raise the quality of ghost imaging. Specifically, we resort to a statistical learning strategy by representing each image patch with sparse coefficients upon an over-complete dictionary. The dictionary is composed of various primitives learned from a large number of image patches from a natural image database. By introducing a linear mapping between non-overlapping image patches and the whole image, we incorporate the above local prior into the convex optimization framework of compressive ghost imaging. Experiments demonstrate that our method could obtain better reconstruction from the same amount of measurements, and thus reduce the number of requisite measurements for achieving satisfying imaging quality.
鬼成像已经迅速发展了大约二十年，并引起了不同研究领域的广泛关注。然而，由于其低重建质量和大量所需测量，鬼成像的实际应用仍然受到很大限制。受自然图像块通常呈现简单结构且这些结构共享共同基元这一事实的启发，我们提出了一种基于块-基元驱动的重建方法来提高鬼成像的质量。具体来说，我们采用一种统计学习策略，通过在一个超完备字典上用稀疏系数表示每个图像块。该字典由从自然图像数据库中的大量图像块学习到的各种基元组成。通过引入非重叠图像块与整个图像之间的线性映射，我们将上述局部先验纳入压缩鬼成像的凸优化框架。实验表明，我们的方法可以从相同数量的测量中获得更好的重建效果，从而减少实现令人满意的成像质量所需的测量数量。
