Multi-resolution analysis enables fidelity-ensured deconvolution for fluorescence microscopy
Open accessAbstract
Fluorescence microscopic imaging is essentially a convolution process distorted by random noise, limiting critical parameters such as imaging speed, duration, and resolution. Though algorithmic compensation has shown great potential to enhance these pivotal aspects, its fidelity remains questioned. Here we develop a physics-rooted computational resolution extension and denoising method with ensured fidelity. Our approach employs a multi-resolution analysis (MRA) framework to extract the two main characteristics of fluorescence images against noise: across-edge contrast, and along-edge continuity. By constraining the two features in a model-solution framework using framelet and curvelet, we develop MRA deconvolution algorithms, which improve the signal-to-noise ratio (SNR) up to 10 dB higher than spatial derivative based penalties, and can provide up to two-fold fidelity-ensured resolution improvement rather than the artifact-prone Richardson-Lucy inference. We demonstrate our methods can improve the performance of various diffraction-limited and super-resolution microscopies with ensured fidelity, enabling accomplishments of more challenging imaging tasks.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A theory for multiresolution signal decomposition: the wavelet representation
Mallat, S. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.192463 · Cited by 21,463 · see citing works
A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
Beck, A., & Teboulle, M. (2009) · SIAM Journal on Imaging Sciences · doi:10.1137/080716542 · Cited by 11,952 · see citing works
Imaging Intracellular Fluorescent Proteins at Nanometer Resolution
Betzig, E., Patterson, G. H., Sougrat, R., Lindwasser, O. W., Olenych, S. G., Bonifacino, J. S., Davidson, M. W., Lippincott‐Schwartz, J., & Hess, H. F. (2006) · Science · doi:10.1126/science.1127344 · Cited by 8,802 · see citing works
Orthonormal bases of compactly supported wavelets
Daubechies, I. C. (1988) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.3160410705 · Cited by 8,143 · see citing works
Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)
Rust, M. J., Bates, M., & Zhuang, X. (2006) · Nature Methods · doi:10.1038/nmeth929 · Cited by 8,109 · see citing works
The wavelet transform, time-frequency localization and signal analysis
Daubechies, I. (1990) · IEEE Transactions on Information Theory · doi:10.1109/18.57199 · Cited by 6,483 · see citing works
Hell, S. W., & Wichmann, J. (1994) · Optics Letters · doi:10.1364/ol.19.000780 · Cited by 6,068 · see citing works
Atomic Decomposition by Basis Pursuit
Chen, S. S., Donoho, D. L., & Saunders, M. A. (2001) · SIAM Review · doi:10.1137/s003614450037906x · Cited by 5,030 · see citing works
Gustafsson, M. G. L. (2000) · Journal of Microscopy · doi:10.1046/j.1365-2818.2000.00710.x · Cited by 3,786 · see citing works
Decomposition of Hardy Functions into Square Integrable Wavelets of Constant Shape
Großmann, A., & Morlet, J. (1984) · SIAM Journal on Mathematical Analysis · doi:10.1137/0515056 · Cited by 3,642 · see citing works
Fast Discrete Curvelet Transforms
Candès, E., Demanet, L., Donoho, D., & Ying, L. (2006) · Multiscale Modeling & Simulation · doi:10.1137/05064182x · Cited by 2,573 · see citing works
Koenderink, J. J. (1984) · Biological Cybernetics · doi:10.1007/bf00336961 · Cited by 2,452 · cited 1× · see citing works
“Later, the theory of multi-resolution analysis (MRA) [23–25] that decomposes the signal into a series of resolution levels was proposed, which provides a novel idea for signal space segmentation.”
Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification
Arganda‐Carreras, I., Kaynig, V., Rueden, C., Eliceiri, K. W., Schindelin, J., Cardona, A., & Seung, H. S. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx180 · Cited by 2,408 · see citing works
Zur Theorie der orthogonalen Funktionensysteme
Haar, A. (1910) · Mathematische Annalen · doi:10.1007/bf01456326 · Cited by 2,164 · see citing works
Multifrequency channel decompositions of images and wavelet models
Mallat, S. (1989) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/29.45554 · Cited by 1,957 · see citing works
Lichtman, J. W., & Conchello, J.-A. (2005) · Nature Methods · doi:10.1038/nmeth817 · Cited by 1,751 · see citing works
Candès, E. J., & Donoho, D. L. (2003) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.10116 · Cited by 1,511 · see citing works
A general framework for image fusion based on multi-scale transform and sparse representation
Liu, Y., Liu, S., & Wang, Z. (2014) · Information Fusion · doi:10.1016/j.inffus.2014.09.004 · Cited by 1,437 · see citing works
Structure and function of ER membrane contact sites with other organelles
Phillips, M. J., & Voeltz, G. K. (2016) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2015.8 · Cited by 933 · see citing works
Framelets: MRA-based constructions of wavelet frames
Daubechies, I. C., Han, B., Ron, A., & Shen, Z. (2003) · Applied and Computational Harmonic Analysis · doi:10.1016/s1063-5203(02)00511-0 · Cited by 734 · see citing works
The functional universe of membrane contact sites
Prinz, W. A., Toulmay, A., & Balla, T. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0180-9 · Cited by 729 · see citing works
Image compression using the 2-D wavelet transform
Lewis, A. S., & Knowles, G. (1992) · IEEE Transactions on Image Processing · doi:10.1109/83.136601 · Cited by 718 · see citing works
The finite ridgelet transform for image representation
Do, M. N., & Vetterli, M. (2003) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.806252 · Cited by 710 · see citing works
Fast, long-term, super-resolution imaging with Hessian structured illumination microscopy
Huang, X., Fan, J., Li, L., Liu, H., Wu, R., Wu, Y., Wei, L., Mao, H., Lal, A., Xi, P., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4115 · Cited by 663 · see citing works
Self-citationWavelet filter evaluation for image compression
Villasenor, J. D., Belzer, B. J., & Liao, J. (1995) · IEEE Transactions on Image Processing · doi:10.1109/83.403412 · Cited by 607 · see citing works
Image denoising review: From classical to state-of-the-art approaches
Goyal, B., Dogra, A., Agrawal, S., Sohi, B. S., & Sharma, A. M. (2019) · Information Fusion · doi:10.1016/j.inffus.2019.09.003 · Cited by 466 · see citing works
Evaluation and development of deep neural networks for image super-resolution in optical microscopy
Qiao, C. F., Li, D., Guo, Y., Liu, C., Jiang, T., Dai, Q., & Li, D. (2021) · Nature Methods · doi:10.1038/s41592-020-01048-5 · Cited by 464 · see citing works
Nixon‐Abell, J., Obara, C. J., Weigel, A. V., et al. (2016) · Science · doi:10.1126/science.aaf3928 · Cited by 459 · see citing works
Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy
Zhao, W., Zhao, S., Li, L., Huang, X., Xing, S., Zhang, Y., Qiu, G., Han, Z., Shang, Y., Sun, D., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-01092-2 · Cited by 417 · see citing works
Bredfeldt, J. S., Liu, Y., Pehlke, C. A., Conklin, M. W., Szulczewski, J. M., Inman, D. R., Keely, P. J., Nowak, R. D., Mackie, T., & Eliceiri, K. W. (2014) · Journal of Biomedical Optics · doi:10.1117/1.jbo.19.1.016007 · Cited by 413 · see citing works
Ultra-stable organic fluorophores for single-molecule research
Zheng, Q., Juette, M. F., Jockusch, S., Wasserman, M. R., Zhou, Z., Altman, R. B., & Blanchard, S. C. (2013) · Chemical Society Reviews · doi:10.1039/c3cs60237k · Cited by 402 · see citing works
Parameter-free image resolution estimation based on decorrelation analysis
Descloux, A. C., Grußmayer, K. S., & Rađenović, A. (2019) · Nature Methods · doi:10.1038/s41592-019-0515-7 · Cited by 372 · see citing works
Quantitative mapping and minimization of super-resolution optical imaging artifacts
Culley, S., Albrecht, D., Jacobs, C., Pereira, P. M., Leterrier, C., Mercer, J., & Henriques, R. (2018) · Nature Methods · doi:10.1038/nmeth.4605 · Cited by 357 · see citing works
Hybrid Rhodamine Fluorophores in the Visible/NIR Region for Biological Imaging
Wang, L., Du, W., Hu, Z., Uvdal, K., Li, L., & Huang, W. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201901061 · Cited by 349 · see citing works
The Use of Sieves to Stabilize Images Produced with the EM Algorithm for Emission Tomography
Snyder, D. L., & Miller, M. I. (1985) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.1985.4334521 · Cited by 278 · see citing works
Deep learning enables structured illumination microscopy with low light levels and enhanced speed
Jin, L., Liu, B., Zhao, F., Hahn, S., Dong, B., Song, R., Elston, T. C., Xu, Y., & Hahn, K. M. (2020) · Nature Communications · doi:10.1038/s41467-020-15784-x · Cited by 255 · see citing works
Wang, Z., Zhu, L., Zhang, H., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01058-x · Cited by 242 · see citing works
Framelet-Based Blind Motion Deblurring From a Single Image
Cai, J., Ji, H., Liu, C., & Shen, Z. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2164413 · Cited by 235 · see citing works
Chen, J., Sasaki, H., Lai, H., Su, Y. P., Liu, J., Wu, Y., Zhovmer, A. S., Combs, C. A., Rey‐Suarez, I., Chang, H.-Y., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01155-x · Cited by 228 · see citing works
Qiao, C. F., Li, D., Liu, Y., Zhang, S., Liu, C., Liu, C., Guo, Y., Jiang, T., Fang, C., Li, N., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01471-3 · Cited by 216 · see citing works
Video denoising using 2D and 3D dual-tree complex wavelet transforms
Selesnick, I., & Li, K. Y. (2003) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.504896 · Cited by 190 · see citing works
Li, X., Li, Y., Zhou, Y., Wu, J., Zhao, Z., Fan, J., Deng, F., Wu, Z., Xiao, G., He, J., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01450-8 · Cited by 184 · see citing works
FALCON: fast and unbiased reconstruction of high-density super-resolution microscopy data
Min, J., Vonesch, C., Kirshner, H., Carlini, L., Olivier, N., Holden, S. J., Manley, S., Ye, J. C., & Unser, M. A. (2014) · Scientific Reports · doi:10.1038/srep04577 · Cited by 153 · see citing works
A review of Bandlet methods for geometrical image representation
Mallat, S., & Gabriel Peyré, A. (2007) · Numerical Algorithms · doi:10.1007/s11075-007-9092-4 · Cited by 140 · see citing works
Spatially adaptive wavelet-based multiscale image restoration
Banham, M. R., & Katsaggelos, A. K. (1996) · IEEE Transactions on Image Processing · doi:10.1109/83.491338 · Cited by 128 · see citing works
Incorporating the image formation process into deep learning improves network performance
Li, Y., Su, Y. P., Guo, M. Y., Han, X., Liu, J., Vishwasrao, H. D., Li, X., Christensen, R. P., Sengupta, T., Moyle, M. W., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01652-7 · Cited by 124 · see citing works
Event-driven acquisition for content-enriched microscopy
Mahečić, D., Stepp, W. L., Zhang, C., Griffié, J., WEIGERT, M. G., & Manley, S. (2022) · Nature Methods · doi:10.1038/s41592-022-01589-x · Cited by 118 · see citing works
Alvelid, J., Damenti, M., Sgattoni, C., & Testa, I. (2022) · Nature Methods · doi:10.1038/s41592-022-01588-y · Cited by 104 · see citing works
Filtering and deconvolution by the wavelet transform
Starck, J., & Bijaoui, A. (1994) · Signal Processing · doi:10.1016/0165-1684(94)90211-9 · Cited by 99 · see citing works
Image reconstruction for structured-illumination microscopy with low signal level
Chu, K., McMillan, P. J., Smith, Z. J., et al. (2014) · Optics Express · doi:10.1364/oe.22.008687 · Cited by 91 · see citing works
Sparse deconvolution of high-density super-resolution images
Hugelier, S., de Rooi, J. J., Bernex, R., et al. (2016) · Scientific Reports · doi:10.1038/srep21413 · Cited by 78 · see citing works
Resolution doubling in light-sheet microscopy via oblique plane structured illumination
Chen, B., Chang, B.-J., Roudot, P., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01635-8 · Cited by 77 · see citing works
Practical considerations for quantitative light sheet fluorescence microscopy
Hobson, C. M., Guo, M. Y., Vishwasrao, H. D., Wu, Y., Shroff, H., & Chew, T. (2022) · Nature Methods · doi:10.1038/s41592-022-01632-x · Cited by 54 · see citing works
Fang, C., Yu, T., Chu, T., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20329-3 · Cited by 49 · see citing works
Ren, W., Ge, X., Li, M., Sun, J. L., Li, S., Gao, S., Shan, C., Gao, B., & Xi, P. (2024) · Light Science & Applications · doi:10.1038/s41377-024-01463-9 · Cited by 49 · see citing works
Self-citationSuperresolution characterization of core centriole architecture
Tian, Y., Wei, C., He, J., et al. (2021) · The Journal of Cell Biology · doi:10.1083/jcb.202005103 · Cited by 33 · see citing works
Fast and simple super-resolution with single images
Eilers, P. H. C., & Ruckebusch, C. (2022) · Scientific Reports · doi:10.1038/s41598-022-14874-8 · Cited by 16 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Organic small-molecule NIR-II fluorophores for tumor phototheranostics
Xiang, D., Wang, Z., Zheng, H., Tang, Y., & Li, Q. (2026) · Light: Science & Applications · doi:10.1038/s41377-026-02212-w · Cited by 22 · see citing works
- Authors
- Hou, Y., Wang, W., Fu, Y., Ge, X., Li, M., & Xi, P.
- Affiliations
- Peking University (CN)
- Corresponding author
- Li, Meiqi
- Xi, Peng
- Venue
- eLight
- Published
- 2024-08-06
- Volume
- 4
- Issue
- 1
- ISSN
- 2097-1710 (ISSN-L)
- 2662-8643
- OpenAlex
- W4401365947
- Cited by
- 42
- References
- 57
- Citations by year
- 2026: 20 · 2025: 21 · 2024: 1
- FWCI
- 5.91
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- eLight (publishedVersion, cc-by)
- Funders
- National Key R&D Program of China (2022YFC3401100)
- National Natural Science Foundation of China (62025501, 31971376, 92150301, 62335008)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
- Keywords
- fluorescence microscopy, deconvolution, multiresolution analysis, image denoising, framelets, curvelets, signal-to-noise ratio improvement, resolution enhancement, Richardson-Lucy deconvolution, super-resolution microscopy, edge continuity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
