Research

Microscopy: system & algorithm

We are focusing on developing advanced optical microscopy for biomedical applications. He particularly is interested in new imaging physics, bottom-up opto-electronic system design, as well as harnessing / designing optimal algorithms / optics for every specific application scenario.

The techniques developed included the
(1) Sparse deconvolution algorithm for extending spatial resolution, contrast and SNR of fluorescence microscopes limited by the optics (applications: Sparse-SIM & Sparse SD-SIM);
(2) PANEL (pixel-level analysis of error locations) framework for quantitatively mapping reconstruction errors at super-resolution scale, which can assess both conventional and deep-learning methods;
(3) SACD (super-resolution method based on the auto-correlation with two-step deconvolution), a fully automatic super-resolution method that can realize a twofold 3D resolution improvement using only 20 frames without needing additional optical components.

Related technologies:
Label-free: Endoscopic/label-free-super-resolution/optical-diffraction-tomography microscopy;
Fluorescence: (miniaturized) SIM/SOFI/SMLM/light-field/multi-photon/confocal microscopy.

Machine learning: method & application

We are using the available prior knowledge to design specific algorithms to analyze and reconstruct the multi-dimensional bio/natural signals (non-deep model). On the other hand, taking advantage of priors from data, deep-learning has the fascinating character (e.g., automatic feature extraction) on solving the problems that can never be solved by conventional ways. He is also using deep-learning to reduce the photon / time / hardware budgets of microscopy, as well as employing (self-supervised, representation, Bayesian) deep-learning to speed up the data profiling pipeline of biologists.

The techniques developed included the
(1) SN2N (self-inspired Noise2Noise), an unsupervised learning-to-denoise engin, which is fully competitive with the supervised learning methods and overcomes the need for large dataset and clean ground-truth.


Related technologies:
Non-deep model: Deconvolution, denoise, phase retrieval, optical tracking, segmentation, and Bayesian inference.
Deep-learning: Cross-modality, low-level transformation, segmentation, visual-relationship.

Biomedical application & Smart bioimage analysis

We are devising high-level algorithms for bioimage analysis to help the biologists to automatically analyze large amounts of image data, reproducibly extract quantitative information from images, and quantify the dynamics, form, and structure of cells and organisms.

The techniques developed included the
(1) Smart palm-size optofluidic hematology analyzer for automated imaging-based leukocyte concentration detection.

(2) MIRD a highly flexible, compact, single-shot volumetric, high-resolution, endoscopic probe by employing precision-machined multiple micro-imaging devices (MIRD).

(3) Sparse confocal microscopy with single particle tracking for uncover that SARS-CoV-2 virus-like particles utilize filopodia to reach the entry site in two patterns, “surfing” and “grabbing”, which avoid the virus from randomly searching on the plasma membrane..

News from the Lab

2024-09: Our SN2N work is online at Nature Methods;

2024-08: Dr. Weisong Zhao has been supported by the National Science Foundation for Outstanding Young Scholars of China;

2024-06: Congrats to Liying Qu & Yuanyuan Huang, the Ph.D. students from the lab. SN2N has been accepted by Nature Methods for publication;

2024-01: Congrats to Liying Qu & Yuanyuan Huang, the Ph.D. students from the lab. SN2N is released as bioRxiv pre-print (under review at Nature Methods) and Python toolkit;

2024-01: Dr. Ke Xu wrote a News & views at Light: Science & Applications entitled "Mapping super-resolution image quality" for our rFRC work;

2024-01: Weisong Zhao has passed the new position at Harbin Institute of Technology as an Full Professor;

2024-01: Weisong Zhao now serves as an Associate Editor at npj Imaging;



2023-12: The rFRC is online at Light: Science & Applications;

2023-12: Post a 'behind the paper' blog regarding the rFRC at Nature Research Community;

2023-12: Congrats to Deer Su, the Ph.D. student from the lab. The Smart palm-size optofluidic hematology analyzer is online at Opto-Electronic Science (invited);

2023-12: Congrats to Deer Su, the Ph.D. student from the lab. The MIRD volumetric endoscope is online at Optics Letters, and is selected as the featured image of VOLUME 48, ISSUE 24;

2023-09: Dr. David Baddeley wrote a News & views at Nature Photonics entitled "Deconvolution enhances fluctuation detection" for our SACD work;

2023-09: The SACD is selected as the cover article of Nature Photonics Volume 17 Issue 9, September 2023;

2023-08: Using Sparse deconvolution-enhanced confocal microscopy, we (with Yaming Jiu's group) reveal the processes of SARS-CoV-2 regulating and utilizing dynamic filopodia for viral invasion, published online at Science Bulletin;

2023-06: Post a 'behind the paper' blog regarding the SACD at Nature Research Community;

2023-06: The SACD is online at Nature Photonics;

2022-12: PANEL and SACD are released as pre-prints;

2022-10: PANELpy is fully open-source;

2022-07: Join Harbin Institute of Technology as an Assistant Professor;

2022-05: SACDm and SACDj are fully open-source;

2021-11: A Python version with GPU accelartion of the Sparse deconvolution is released at GitHub;

2021-11: Post a 'behind the paper' blog about the Sparse deconvolution at Nature Research Community;

2021-11: The Sparse deconvolution is online at Nature Biotechnology;

2021-07: PANELM and PANELJ are fully open-source;

2021-07: The Sparse deconvolution is accepted by Nature Biotechnology for publication;

2021-03: An OPEN scientific discussion about deconvolution is posted on GitHub as well as on Twitter;

2020-11: The Sparse deconvolution is fully open-source;

2020-09: Invited talk on SPIE Optical Engineering + Applications, with a title Ultrafast super-resolution imaging via auto-correlation two-step deconvolution at Volume 11497, Ultrafast Nonlinear Imaging and Spectroscopy VIII; 114970V (2020)

2020-08: The Sparse deconvolution is reviewed in Nature Biotechnology by three referees;

2020-01: The Sparse deconvolution is reviewed in Nature by three referees;

2020-01: img2vid v0.1.0 is released; Adaptive median filter imagej plugin v0.1.0 is released; The Sparse deconvolution is released with .p code;
2019-08: Visiting Martin Booth's lab and National physics lab in UK;   NPL