Research

We develop biomedical imaging technologies spanning imaging physics, system design, computational reconstruction, machine learning, quantitative bioimage analysis, and compact biomedical devices.

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Microscopy systems and algorithms

Microscopy: Systems & Algorithms

We develop biomedical imaging technologies by combining new imaging physics, bottom-up system design, and hardware–algorithm co-design for specific imaging scenarios.

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We focus on developing advanced biomedical imaging technologies across modalities. Our research spans new imaging physics, bottom-up system design, and the co-design of hardware and computational algorithms tailored to specific imaging scenarios. We aim to optimize spatial, temporal, and information resolution under practical constraints such as photon or signal budget, acquisition speed, imaging depth, and system complexity.

Representative developed techniques

Sparse deconvolution

A physics-informed reconstruction framework that exploits sparsity and structural continuity to enhance resolution, contrast, and SNR beyond the limits of microscopy, particularly under low-SNR conditions. Applications include Sparse-SIM and Sparse SD-SIM.

Sparse deconvolution project →
SACD

A fluctuation-based super-resolution framework that achieves approximately threefold lateral and axial resolution enhancement using only 20 frames, without additional optical components. Sparse-SACD further enables fast 4D live-cell super-resolution imaging.

See also our label-free SACD [Light: Science & Applications, 2025].

SACD project →
RIED

A reaction-luminescence super-resolution framework for extremely photon-limited bioluminescence and electrochemiluminescence. It enables high-contrast 2D and 3D imaging without external optical excitation or photochemical modulation, including continuous BL-RIED imaging for over two days.

RIED project →
Machine learning methods and applications

Machine Learning: Methods & Applications

We combine explicit physical and structural priors with data-driven learning to reconstruct and analyze multidimensional biomedical and natural signals.

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We develop machine-learning methods for the reconstruction and analysis of multidimensional biomedical and natural signals. We exploit both explicit physical and structural priors to design interpretable computational models, and data-driven priors to address problems that are difficult to solve with conventional methods. Applications range from reducing the photon, acquisition-time, and hardware requirements of microscopy to accelerating biological image reconstruction, profiling, and discovery.

Representative developed techniques

SN2N

Self-inspired Noise2Noise is a self-supervised learning-to-denoise framework that performs competitively with supervised learning without large paired datasets or clean ground-truth images. It supports robust, photon-efficient, long-term, and low-phototoxicity imaging.

See also adaptive SN2N [PhotoniX Life, 2026].

SN2N project →
aSN2N

Adaptive SN2N introduces adaptive preprocessing and normalization to stabilize self-supervised learning across heterogeneous imaging conditions, improving robustness to intensity variation and background contamination while suppressing reconstruction artifacts.

aSN2N project →
Smart bioimage analysis and biomedical devices

Smart Biomedical Image-Analysis & Imaging-Device

We develop quantitative biomedical image analysis methods and compact, application-oriented imaging instrumentation for automated, high-throughput biomedical investigation.

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We develop intelligent biomedical image analysis methods and biomedical imaging instrumentation to enable automated, quantitative, and high-throughput biological investigation. Our work spans image segmentation, tracking, profiling, and representation learning, as well as compact, application-oriented imaging devices and systems.

Representative developed techniques

PANEL

Pixel-level analysis of error locations is a quantitative framework for mapping reconstruction errors at the super-resolution scale and systematically assessing conventional and deep-learning reconstruction methods.

PANEL project →
Smart palm-size optofluidic hematology analyzer

A compact imaging-based platform for automated leukocyte concentration measurement and rapid hematological analysis.

Smart hematology analyzer project →
Light-field endoscopic probes

Compact and flexible probes, initiated with MIRD, for single-shot high-resolution volumetric imaging, supported by new optical designs and computational imaging strategies.

See also [Optics and Lasers in Engineering, 2026] and [Optics Express, 2026].

Light-field endoscopy project →
Sparse confocal microscopy with single-particle tracking

This approach revealed how SARS-CoV-2 virus-like particles exploit filopodia through “surfing” and “grabbing” to reach cellular entry sites and reduce random searching on the plasma membrane.

See also [European Journal of Cell Biology, 2025].

Sparse confocal microscopy project →

News

Selected publications, research milestones, and updates from the lab.

News Cards

RIED paper preview X · August 12, 2026 RIED is out at Nature A new paradigm for excitation-free super-resolution microscopy using ECL, CL, and BL. View post on X ↗ SN2N paper preview X · September 11, 2024 SN2N is out at Nature Methods An unsupervised denoising solution competitive with supervised learning, trained from a single noisy frame without clean ground truth. View post on X ↗ Mapping super-resolution image quality paper preview Light: Science & Applications · News & ViewsMapping super-resolution image qualityA News & Views article by Ke Xu discussing our rFRC work for local super-resolution image-quality mapping.Read the article ↗ npj Imaging editorial board preview npj Imaging · Editorial BoardAssociate Editor at npj ImagingThe lab PI now serves as an Associate Editor at npj Imaging.View the editorial board ↗ rFRC paper preview X · December 14, 2023 rFRC is out at Light: Science & Applications A tool for evaluating super-resolution image quality at the corresponding super-resolution scale. View post on X ↗ Deconvolution enhances fluctuation detection paper preview Nature Photonics · News & ViewsDeconvolution enhances fluctuation detectionA News & Views article by David Baddeley discussing our SACD work and its two-step deconvolution workflow.Read the article ↗ Nature Photonics Volume 17 Issue 9 cover featuring SACD Nature Photonics · Cover · September 2023Efficient super-resolution imagingThe cover shows a wide-field super-resolution image produced by autocorrelation and two-step deconvolution. With only 20 frames, the method doubles axial and lateral resolution and captures thousands of cells across 2 mm × 1.4 mm in 10 minutes at 128 nm resolution.View the issue ↗ SACD paper preview X · June 15, 2023 SACD is out at Nature Photonics SACD enhances fluorescence-fluctuation detection sensitivity by orders of magnitude and needs only 20 frames for >2-fold 3D resolution. View post on X ↗

Latest News

  • Our RIED work is online at Nature.
  • Congrats to Jiahui Gui, the Ph.D. student from the lab and co-first author, on the acceptance of our RIED work in Nature!
  • Congrats to Deer Su, the Ph.D. student from the lab, on successfully defending his Ph.D. dissertation!
  • Congrats to Deer Su, the Ph.D. student from the lab. Two studies advancing light-field endoscopic probes have been published in Optics and Lasers in Engineering and Optics Express.
  • Congrats to Jiahui Gui, the Ph.D. student from the lab. RIED is released as a bioRxiv preprint with its MATLAB toolkit.
Complete news archive

News from the Lab

2026-08: Our RIED work is online at Nature;

2026-07: Congrats to Jiahui Gui, the Ph.D. student from the lab and co-first author, on the acceptance of our RIED work in Nature!

2026-04: Congrats to Deer Su, the Ph.D. student from the lab, on successfully defending his Ph.D. dissertation!

2026-04: Congrats to Deer Su, the Ph.D. student from the lab. Two studies advancing light-field endoscopic probes have been published in Optics and Lasers in Engineering and Optics Express;

2026-02: Congrats to Jiahui Gui, the Ph.D. student from the lab. RIED is released as a bioRxiv preprint, together with the MATLAB toolkit;
2025-09: The lab PI has been promoted to Tenured Professor at Harbin Institute of Technology;

2025-09: Congrats to Liying Qu, the Associate Investigator from the lab. Our commentary on SN2N is online at Clinical and Translational Medicine;

2025-08: FLAME is released as a preprint, together with its MATLAB toolkit;

2025-08: Our collaborative work, EPSLON, has been published in Light: Science & Applications;

2025-07: SR-Wiki is now the lab’s official GitHub organization for releasing our latest imaging and analysis tools and provides implementations of the lab’s imaging toolkit.

2025-06: Congrats to Jingyang Zhu, the Ph.D. student from the lab. Adaptive SN2N has been published in PhotoniX Life;

2025-04: Congrats to Liying Qu on successfully defending her Ph.D. dissertation;
2024-09: Our SN2N work is online at Nature Methods;

2024-08: The lab PI has received support from the Excellent Young Scientists Fund of the National Natural Science Foundation 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 a bioRxiv preprint (under review at Nature Methods), together with its Python toolkit;

2024-01: Dr. Ke Xu wrote a News & Views in Light: Science & Applications entitled “Mapping super-resolution image quality” for our rFRC work;

2024-01: The lab PI has been appointed as a Full Professor;

2024-01: The lab PI now serves as an Associate Editor at npj Imaging;
2023-12: The rFRC work 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 article, “Deconvolution enhances fluctuation detection,” in Nature Photonics 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
2022-12: PANEL and SACD are released as preprints;

2022-10: PANELpy is fully open-source;

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

2022-05: SACDm and SACDj are fully open-source;
2021
2021-11: A Python version with GPU acceleration 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;