Structured illumination microscopy (SIM) reconstructs a super-resolved image from multiple raw images captured with different illumination patterns; hence, acquisition speed is limited, making it unsuitable for dynamic scenes. We propose a new method, Speckle Flow SIM, that uses static patterned illumination with moving samples and models the sample motion during data capture in order to reconstruct the dynamic scene with super-resolution. Speckle Flow SIM relies on sample motion to capture a sequence of raw images. The spatio-temporal relationship of the dynamic scene is modeled using a neural space-time model with coordinate-based multi-layer perceptrons (MLPs), and the motion dynamics and the super-resolved scene are jointly recovered. We validate Speckle Flow SIM for coherent imaging in simulation and build a simple, inexpensive experimental setup with off-the-shelf components. We demonstrate that Speckle Flow SIM can reconstruct a dynamic scene with deformable motion and 1.88x the diffraction-limited resolution in experiment.
翻译:结构化光化显微镜( SIM) 从不同光化模式所捕捉的多张原始图像中重建一个超级解析图像; 因此, 获取速度有限, 无法适应动态场景 。 我们提出一种新的方法, 即 Speckle Flow SIM, 使用移动样本和模型静态模式照明, 在数据采集过程中使用静态模式照明, 以便用超分辨率重建动态场景 。 Speckle Flow SIM 依靠样本运动来捕捉一系列原始图像 。 动态场景的瞬时关系使用一个以协调为基础的多层透视器( MLPs) 的神经空间时间模型来建模, 并且联合恢复运动动态和超级解析场景 。 我们验证 Speckle Flow SIM, 用于模拟连贯的成像, 并用现成部件建立一个简单、 廉价的实验场景。 我们证明 Speckle flow SIM 可以以变形动作和1.88x 实验中受裂分解的分辨率来重建一个动态场景。