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DeconvolutionLab2: An open-source software for deconvolution microscopy
Methods, 2017 Images in fluorescence microscopy are inherently blurred due to the limit of diffraction of light. The purpose of deconvolution microscopy is to compensate numerically for this degradation.
Ferreol Soulez +2 more
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SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer [PDF]
IEEE International Conference on Computer Vision, 2021 Point cloud completion aims to predict a complete shape in high accuracy from its partial observation. However, previous methods usually suffered from discrete nature of point cloud and unstructured prediction of points in local regions, which makes it ...
Peng Xiang +6 more
semanticscholar +1 more source
THE DIRECT-INVERSION DECONVOLUTION AND ITS APPLICATION IN SEISMIC DATA
JGE, 2022 Seismic traces are generated by the convolution of reflectivity and seismic wavelet. Due to limited frequency bandwidth, reflectivity can not be resolved easily.
Iktri Madrinovella, Waskito Pranowo
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Blind Deconvolution Based on Correlation Spectral Negentropy for Bearing Fault
Entropy, 2023 Blind deconvolution is a method that can effectively improve the fault characteristics of rolling bearings. However, the existing blind deconvolution methods have shortcomings in practical applications.
Tian Tian +3 more
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bioRxiv, 2023 Spatial transcriptomics technologies generate gene expression profiles with spatial context, requiring spatially informed analysis tools for three key tasks, spatial clustering, multisample integration, and cell-type deconvolution.
Yahui Long +15 more
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Learning Deconvolution Network for Semantic Segmentation [PDF]
IEEE International Conference on Computer Vision, 2015 We propose a novel semantic segmentation algorithm by learning a deep deconvolution network. We learn the network on top of the convolutional layers adopted from VGG 16-layer net.
Hyeonwoo Noh +2 more
semanticscholar +1 more source
Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics
Nature Biotechnology, 2022 Many spatially resolved transcriptomic technologies do not have single-cell resolution but measure the average gene expression for each spot from a mixture of cells of potentially heterogeneous cell types.
Ying Ma, Xiang Zhou
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Molecular Therapy: Methods & Clinical Development, 2023 Cell therapy for Parkinson’s disease has experienced substantial growth in the past decades with several ongoing clinical trials. Despite increasing refinement of differentiation protocols and standardization of the transplanted neural precursors, the ...
Jana Rájová +11 more
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Nature Communications, 2023 Spatial transcriptomics technologies are used to profile transcriptomes while preserving spatial information, which enables high-resolution characterization of transcriptional patterns and reconstruction of tissue architecture.
Haoyang Li +9 more
semanticscholar +1 more source
Sensors, 2023 Many works in the state of the art are interested in the increase of the camera depth of field (DoF) via the joint optimization of an optical component (typically a phase mask) and a digital processing step with an infinite deconvolution support or a ...
Alice Fontbonne +4 more
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