Deep learning-based virtual refocusing of images using an engineered point-spread function

PS$^2$F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing

We propose a compact snapshot monocular depth estimation technique that relies on an engineered point spread function (PSF). Traditional approaches used in microscopic super-resolution imaging such as the Double-Helix PSF (DHPSF) are ill-suited for scenes that are more complex than a sparse set of point light sources. We show, using the Cramér-Rao lower bound, that separating the two lobes of the DHPSF and thereby capturing two separate images leads to a dramatic increase in depth accuracy. A special property of the phase mask used for generating the DHPSF is that a separation of the phase mas…

DeepSTORM3D: dense three dimensional localization microscopy and point spread function design by deep learning

Localization microscopy is an imaging technique in which the positions of individual nanoscale point emitters (e.g. fluorescent molecules) are determined at high precision from their images. This is the key ingredient in single/multiple-particle-tracking and several super-resolution microscopy approaches. Localization in three-dimensions (3D) can be performed by modifying the image that a point-source creates on the camera, namely, the point-spread function (PSF). The PSF is engineered using additional optical elements to vary distinctively with the depth of the point-source. However, localizi…

CodedEvents：面向事件相机三维追踪的点扩散函数优化设计

CodedEvents: Optimal Point-Spread-Function Engineering for 3D-Tracking with Event Cameras

该研究为PSF工程事件相机的三维点定位与追踪建立Cramér Rao界，并基于隐式神经表示设计面向运动点源的最优相位与二值振幅掩模。

用于6D超分辨率显微的偶极子点扩散函数工程

Dipole-Spread Function Engineering for 6D Super-Resolution Microscopy

本文综述了用于6D超分辨率单分子取向-定位显微镜（SMOLM）的偶极子点扩散函数（DSF）工程，涵盖图像形成理论、相位与偏振调制方法、多种PSF/DSF技术比较、成像系统设计及生物学应用与未来挑战。

Spatially-Variant CNN-based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy

Optical microscopy is an essential tool in biology and medicine. Imaging thin, yet non-flat objects in a single shot (without relying on more sophisticated sectioning setups) remains challenging as the shallow depth of field that comes with high-resolution microscopes leads to unsharp image regions and makes depth localization and quantitative image interpretation difficult. Here, we present a method that improves the resolution of light microscopy images of such objects by locally estimating image distortion while jointly estimating object distance to the focal plane. Specifically, we estimat…

End-to-end lossless compression of high precision depth maps guided by pseudo-residual

As a fundamental data format representing spatial information, depth map is widely used in signal processing and computer vision fields. Massive amount of high precision depth maps are produced with the rapid development of equipment like laser scanner or LiDAR. Therefore, it is urgent to explore a new compression method with better compression ratio for high precision depth maps. Utilizing the wide spread deep learning environment, we propose an end-to-end learning-based lossless compression method for high precision depth maps. The whole process is comprised of two sub-processes, named pre-p…