Recent advances in point spread function engineering and related computational microscopy approaches: from one viewpoint

Abstract

This personal hybrid review piece, written in light of my recipience of the UIPAB 2020 young investigator award, contains a mixture of my scientific biography and work so far. This paper is not intended to be a comprehensive review, but only to highlight my contributions to computation-related aspects of super-resolution microscopy, as well as their origins and future directions.

Deep learning in single-molecule imaging and analysis: recent advances and prospects

“…70,71 SMLM and its deep learning applications have been extensively reviewed. 1,[31][32][33][72][73][74][75][76][77] Due to the limited space, we will focus on single-molecule imaging and only mention SMLM briey in the review.…”

“…Finally, we will conclude on the current stage of deep learning in single-molecule imaging and data analysis, discuss the pitfalls of the existing applications, and outline the directions for future development. It should be noted that deep-learning-assisted single-molecule localization microscopy (SMLM), including single molecule localization method [20][21][22] , image reconstruction, 23,24 background estimation, 25 and point spread function (PSF) engineering, [26][27][28][29][30] has received broad attention and been reviewed extensively 1,[31][32][33] . Equally important but often neglected areas are the singlemolecule imaging automation and single-molecule feature recognition, which will be the focus of this review.…”

“…It should be noted that deep-learning-assisted SMLM, including the single molecule localization method, [20][21][22] image reconstruction, 23,24 background estimation, 25 and point spread function (PSF) engineering, [26][27][28][29][30] has received broad attention and been reviewed extensively. 1,[31][32][33] Equally important but oen neglected areas are single-molecule imaging automation and single-molecule feature recognition, which will be the focus of this review.…”

Video Reconstruction from a Single Motion Blurred Image using Learned Dynamic Phase Coding

“…As the end-to-end backpropagation-based optimization process of deep models proved itself to be very efficient for various tasks, its power was also harnessed for optical design, either for a standalone optical system design process or jointly with a post-processing algorithm (for recent review on this topics see [7,50]). Specifically for enhanced optical imaging applications, this scheme had been presented for extended depth of field [4,12,43], depth estimation [9,14,51], high dynamic range [29,45], and several microscopy applications [21,33,40,52], to name a few. Yet, it was not considered for the problem of video from blur.…”

Snapshot super-resolution indirect time-of-flight camera using a grating-based subpixel encoder and depth-regularizing compressive reconstruction

“…To realize snapshot and model-driven SR, compressive sensing (CS) [18,19] with point-spreadfunction (PSF) engineering [20][21][22][23] has been studied [24][25][26][27][28]. To achieve SR, the CS approach engineers the PSF for encoding sub-pixel information in a single measurement.…”