Zero-Shot Denoising for Fluorescence Lifetime Imaging Microscopy with Intensity-Guided Learning
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916687189639168 |
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| author | Chen, Hao Najera, Julian Geresu, Dagmawit Datta, Meenal Smith, Cody Howard, Scott |
| author_facet | Chen, Hao Najera, Julian Geresu, Dagmawit Datta, Meenal Smith, Cody Howard, Scott |
| contents | 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. For FLIM, the intrinsic relationship between intensity and lifetime information means noise in each channel is a multivariate function 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, thereby enabling more reliable extraction of physiological and molecular information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13779 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Zero-Shot Denoising for Fluorescence Lifetime Imaging Microscopy with Intensity-Guided Learning Chen, Hao Najera, Julian Geresu, Dagmawit Datta, Meenal Smith, Cody Howard, Scott Image and Video Processing 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. For FLIM, the intrinsic relationship between intensity and lifetime information means noise in each channel is a multivariate function 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, thereby enabling more reliable extraction of physiological and molecular information. |
| title | Zero-Shot Denoising for Fluorescence Lifetime Imaging Microscopy with Intensity-Guided Learning |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2503.13779 |