CellINR: Implicitly Overcoming Photo-induced Artifacts in 4D Live Fluorescence Microscopy

• URL: http://arxiv.org/abs/2508.19300v1
• Date: Mon, 25 Aug 2025 18:38:25 GMT
• Title: CellINR: Implicitly Overcoming Photo-induced Artifacts in 4D Live Fluorescence Microscopy
• Authors: Cunmin Zhao, Ziyuan Luo, Guoye Guan, Zelin Li, Yiming Ma, Zhongying Zhao, Renjie Wan,
• Abstract summary: We propose CellINR, a case-specific optimization approach based on implicit neural representation.<n>The method employs blind convolution and structure amplification strategies to map 3D spatial coordinates into the high frequency domain.<n> Experimental results demonstrate that CellINR significantly outperforms existing techniques in artifact removal and restoration of structural continuity.
• Score: 24.47926453523119
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 4D live fluorescence microscopy is often compromised by prolonged high intensity illumination which induces photobleaching and phototoxic effects that generate photo-induced artifacts and severely impair image continuity and detail recovery. To address this challenge, we propose the CellINR framework, a case-specific optimization approach based on implicit neural representation. The method employs blind convolution and structure amplification strategies to map 3D spatial coordinates into the high frequency domain, enabling precise modeling and high-accuracy reconstruction of cellular structures while effectively distinguishing true signals from artifacts. Experimental results demonstrate that CellINR significantly outperforms existing techniques in artifact removal and restoration of structural continuity, and for the first time, a paired 4D live cell imaging dataset is provided for evaluating reconstruction performance, thereby offering a solid foundation for subsequent quantitative analyses and biological research. The code and dataset will be public.

Related papers

• FSP-Diff: Full-Spectrum Prior-Enhanced DualDomain Latent Diffusion for Ultra-Low-Dose Spectral CT Reconstruction [20.226435074884808]
  FSP-Diff is a full-spectrum prior-enhanced dual-domain latent diffusion framework for ultra-low-dose spectral CT reconstruction.<n>Our framework integrates direct image reconstructions with projection-domain denoised results.<n>Experiments on simulated and real-world datasets demonstrate that FSP-Diff significantly outperforms state-of-the-art methods in both image quality and computational efficiency.
  arXiv  Detail & Related papers  (2026-02-08T14:13:19Z)
• Accelerating 3D Photoacoustic Computed Tomography with End-to-End Physics-Aware Neural Operators [74.65171736966131]
  Photoacoustic computed tomography (PACT) combines optical contrast with ultrasonic resolution, achieving deep-tissue imaging beyond the optical diffusion limit.<n>Current implementations require dense transducer arrays and prolonged acquisition times, limiting clinical translation.<n>We introduce Pano, an end-to-end physics-aware model that directly learns the inverse acoustic mapping from sensor measurements to volumetric reconstructions.
  arXiv  Detail & Related papers  (2025-09-11T23:12:55Z)
• Beyond Imaging: Vision Transformer Digital Twin Surrogates for 3D+T Biological Tissue Dynamics [0.433313790597694]
  Vision Transformer Digital Twin Surrogate Network (VT-DTSN) is a deep learning framework for predictive modeling of 3D+T imaging data from biological tissue.<n>VT-DTSN learns to reconstruct high-fidelity, time-resolved dynamics of a Drosophila midgut.
  arXiv  Detail & Related papers  (2025-08-21T16:24:24Z)
• CryoFastAR: Fast Cryo-EM Ab Initio Reconstruction Made Easy [58.86576215228903]
  We introduce CryoFastAR, the first geometric foundation model that can directly predict poses from Cryo-EM noisy images for Fast ab initio Reconstruction.<n>By integrating multi-view features and training on large-scale simulated cryo-EM data with realistic noise and CTF modulations, CryoFastAR enhances pose estimation accuracy and generalization.
  arXiv  Detail & Related papers  (2025-06-06T08:32:32Z)
• Large model enhanced computational ghost imaging [44.77879260113026]
  Ghost imaging (GI) achieves 2D image reconstruction through high-order correlation of 1D bucket signals and 2D light field information.<n>Recent investigations have established that deep learning (DL) can substantially enhance the ghost imaging reconstruction quality.<n>We propose the first large imaging model with 1.4 billion parameters that incorporates the physical principles of GI (GILM)
  arXiv  Detail & Related papers  (2025-03-10T03:34:28Z)
• 4DRGS: 4D Radiative Gaussian Splatting for Efficient 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images [49.170407434313475]
  Existing methods often produce suboptimal results or require excessive computation time.<n>We propose 4D radiative Gaussian splatting (4DRGS) to achieve high-quality reconstruction efficiently.<n>4DRGS achieves impressive results in 5 minutes training, which is 32x faster than the state-of-the-art method.
  arXiv  Detail & Related papers  (2024-12-17T13:51:56Z)
• PR-ENDO: Physically Based Relightable Gaussian Splatting for Endoscopy [1.28795255913358]
  3D reconstruction and novel-view synthesis from endoscopic images are promising tools for enhancing diagnosis.<n>We present PR-ENDO, a novel 3D reconstruction framework leveraging the unique property of endoscopic imaging, where a single light source is closely aligned with the camera.<n> PR-ENDO achieves superior reconstruction quality compared to baseline methods on both public and in-house datasets.
  arXiv  Detail & Related papers  (2024-11-19T13:52:30Z)
• OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing [62.993663757843464]
  Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
  arXiv  Detail & Related papers  (2022-06-17T08:11:26Z)
• Neural 3D Reconstruction in the Wild [86.6264706256377]
  We introduce a new method that enables efficient and accurate surface reconstruction from Internet photo collections. We present a new benchmark and protocol for evaluating reconstruction performance on such in-the-wild scenes.
  arXiv  Detail & Related papers  (2022-05-25T17:59:53Z)
• Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction [108.06731611196291]
  We develop a multi-channel convolutional analysis operator learning (MCAOL) method to exploit common spatial features within attenuation images at different energies. We propose an optimization method which jointly reconstructs the attenuation images at low and high energies with a mixed norm regularization on the sparse features.
  arXiv  Detail & Related papers  (2022-03-10T14:22:54Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.