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Self-inspired learning for denoising live-cell super-resolution microscopy
Every collected photon is precious in live-cell super-resolution (SR) microscopy. Here, we
describe a data-efficient, deep learning-based denoising solution to improve diverse SR …
describe a data-efficient, deep learning-based denoising solution to improve diverse SR …
Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy
Computational super-resolution methods, including conventional analytical algorithms and
deep learning models, have substantially improved optical microscopy. Among them …
deep learning models, have substantially improved optical microscopy. Among them …
Delivery and kinetics of immersion optical clearing agents in tissues: Optical imaging from ex vivo to in vivo
Advanced optical imaging provides a powerful tool for the structural and functional analysis
of tissues with high resolution and contrast, but the imaging performance decreases as light …
of tissues with high resolution and contrast, but the imaging performance decreases as light …
Self-supervised denoising for multimodal structured illumination microscopy enables long-term super-resolution live-cell imaging
Detection noise significantly degrades the quality of structured illumination microscopy (SIM)
images, especially under low-light conditions. Although supervised learning based …
images, especially under low-light conditions. Although supervised learning based …
Computational optical imaging: on the convergence of physical and digital layers
Optical imaging has traditionally relied on hardware to fulfill its imaging function, producing
output measures that mimic the original objects. Developed separately, digital algorithms …
output measures that mimic the original objects. Developed separately, digital algorithms …
Robust self-supervised denoising of voltage imaging data using CellMincer
Voltage imaging is a powerful technique for studying neuronal activity, but its effectiveness is
often constrained by low signal-to-noise ratios (SNR). Traditional denoising methods, such …
often constrained by low signal-to-noise ratios (SNR). Traditional denoising methods, such …
Multi-color fluorescence live-cell imaging in Dictyostelium discoideum
H Hashimura, S Kuwana, H Nakagawa… - Cell Structure and …, 2024 - jstage.jst.go.jp
The cellular slime mold Dictyostelium discoideum, a member of the Amoebozoa, has been
extensively studied in cell and developmental biology. D. discoideum is unique in that they …
extensively studied in cell and developmental biology. D. discoideum is unique in that they …
PNR: Physics-informed Neural Representation for high-resolution LFM reconstruction
Light field microscopy (LFM) has been widely utilized in various fields for its capability to
efficiently capture high-resolution 3D scenes. Despite the rapid advancements in neural …
efficiently capture high-resolution 3D scenes. Despite the rapid advancements in neural …
Self-inspired learning to denoise for live-cell super-resolution microscopy
Every collected photon is precious in live-cell super-resolution (SR) fluorescence
microscopy for contributing to breaking the diffraction limit with the preservation of temporal …
microscopy for contributing to breaking the diffraction limit with the preservation of temporal …
Unsupervised multi-animal tracking for quantitative ethology
Quantitative ethology necessitates accurate tracking of animal locomotion, especially for
population-level analyses involving multiple individuals. However, current methods rely on …
population-level analyses involving multiple individuals. However, current methods rely on …