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Found 6 papers, 2 papers with code
Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot Image Restoration
Our approach combines a pre-trained network to extract deep features from the input image with iterative Richardson-Lucy deconvolution steps.
BigFUSE: Global Context-Aware Image Fusion in Dual-View Light-Sheet Fluorescence Microscopy with Image Formation Prior
Light-sheet fluorescence microscopy (LSFM), a planar illumination technique that enables high-resolution imaging of samples, experiences defocused image quality caused by light scattering when photons propagate through thick tissues.
LUCYD: A Feature-Driven Richardson-Lucy Deconvolution Network
Our results demonstrate that LUCYD outperforms the state-of-the-art methods in both synthetic and real microscopy images, achieving superior performance in terms of image quality and generalisability.
DeStripe: A Self2Self Spatio-Spectral Graph Neural Network with Unfolded Hessian for Stripe Artifact Removal in Light-sheet Microscopy
Light-sheet fluorescence microscopy (LSFM) is a cutting-edge volumetric imaging technique that allows for three-dimensional imaging of mesoscopic samples with decoupled illumination and detection paths.
Essential Metadata for 3D BRAIN Microscopy
Recent advances in fluorescence microscopy techniques and tissue clearing, labeling, and staining provide unprecedented opportunities to investigate brain structure and function.
Real-time multi-view deconvolution
In light-sheet microscopy, overall image content and resolution are improved by acquiring and fusing multiple views of the sample from different directions.
