论文标题
基于学习的超级插值和斑点图像重建的外推
Learning-based super interpolation and extrapolation for speckled image reconstruction
论文作者
论文摘要
当连贯的光与生物组织相互作用时，会产生斑点。从斑点检索信息是必需的但具有挑战性的，需要理解或映射多个散射过程，或可靠的能力逆转或补偿散射引起的相扭曲。无论哪种情况，斑点的采样不足都会破坏编码的信息，从而阻碍了斑点模式的成功对象重建。在这项工作中，我们提出了一种对抗物理极限的深度学习方法：子nyquist采样斑点（低于nyquist标准）的插值达到了良好的水平（以平滑的形态良好的形态良好的形态，以达到良好的水平（可以解决同一FOV）。更重要的是，丢失的信息可以追溯，这是经典的插值或任何现有方法是不可能的。学习网络激发了对斑点本质的新观点，以及一个有前途的平台，用于有效地处理或解密大量散射的光学信号，从而在复杂的场景中实现了广阔的高分辨率成像。
Speckles arise when coherent light interacts with biological tissues. Information retrieval from speckles is desired yet challenging, requiring understanding or mapping of the multiple scattering process, or reliable capability to reverse or compensate for the scattering-induced phase distortions. In whatever situation, insufficient sampling of speckles undermines the encoded information, impeding successful object reconstruction from speckle patterns. In this work, we propose a deep learning method to combat the physical limit: the sub-Nyquist sampled speckles (~14 below the Nyquist criterion) are interpolated up to a well-resolved level (1024 times more pixels to resolve the same FOV) with smoothed morphology fine-textured. More importantly, the lost information can be retraced, which is impossible with classic interpolation or any existing methods. The learning network inspires a new perspective on the nature of speckles and a promising platform for efficient processing or deciphering of massive scattered optical signals, enabling widefield high-resolution imaging in complex scenarios.
