Zero-Shot Denoising for Fluorescence Lifetime Imaging Microscopy with Intensity-Guided Learning

Hao Chen, Julian Najera, Dagmawit Geresu, Meenal Datta, Cody Smith, Scott Howard; Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) Workshops, 2025, pp. 4719-4728

Abstract


Multimodal and multi-information microscopy techniques such as Fluorescence Lifetime Imaging Microscopy (FLIM) extend the informational channels beyond intensity-based fluorescence microscopy but suffer from reduced image quality due to complex noise patterns. In FLIM, the intrinsic relationship between intensity and lifetime information means noise in each channel exhibits a multivariate dependence across channels without necessarily sharing structural features. Based on this, we present a novel Zero-Shot Denoising Framework with an Intensity-Guided Learning approach. Our correlation-preserving strategy maintains important biological information that might be lost when channels are processed independently. Our framework implements separate processing paths for each channel and utilizes a pre-trained intensity denoising prior to guide the refinement of lifetime components across multiple channels. Through experiments on real-world FLIM-acquired biological samples, we show that our approach outperforms existing methods in both noise reduction and lifetime preservation, providing more reliable extraction of physiological and molecular information.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Chen_2025_CVPR, author = {Chen, Hao and Najera, Julian and Geresu, Dagmawit and Datta, Meenal and Smith, Cody and Howard, Scott}, title = {Zero-Shot Denoising for Fluorescence Lifetime Imaging Microscopy with Intensity-Guided Learning}, booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) Workshops}, month = {June}, year = {2025}, pages = {4719-4728} }