NanoJ-FluidicsManual, source-code and binaries for the NanoJ-Fluidics project
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picassoA collection of tools for painting super-resolution images
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sparse-deconv-pyOfficial Python implementation of the 'Sparse deconvolution'-v0.3.0
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flikaAn interactive image processing program for biologists written in Python.
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Jalali-Lab-Implementation-of-RAISRImplementation of RAISR (Rapid and Accurate Image Super Resolution) algorithm in Python 3.x by Jalali Laboratory at UCLA. The implementation presented here achieved performance results that are comparable to that presented in Google's research paper (with less than ± 0.1 dB in PSNR). Just-in-time (JIT) compilation employing JIT numba is used to …
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GPimGaussian processes and Bayesian optimization for images and hyperspectral data
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cellfinderAutomated 3D cell detection and registration of whole-brain images
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axondeepsegAxon/Myelin segmentation using Deep Learning
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MLSRSource code for ECCV2020 "Fast Adaptation to Super-Resolution Networks via Meta-Learning"
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SRCNN-PyTorchPytorch framework can easily implement srcnn algorithm with excellent performance
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segmentationCode for my master's thesis. Instance segmentation of fluorescence microscopy images with deep learning.
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Scripts🔬🍸 Home of the ImageJ BAR: A collection of Broadly Applicable Routines for ImageJ
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cyclopsPrecision current source, with optional optical feedback, for driving LEDs and laser diodes
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LLSpyLattice light-sheet post-processing utility.
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tensorflow-bicubic-downsampletf.image.resize_images has aliasing when downsampling and does not have gradients for bicubic mode. This implementation fixes those problems.
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ome-typesnative Python dataclasses for the OME data model
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Magpie将任何窗口放大至全屏
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MiM NikonTiConfig and scripts used to run a Nikon Eclipse Ti in the vanNimwegen lab.
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FBMulti-frame super-resolution via sub-pixel.
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FRETBurstsBurst analysis software for smFRET. **Moved to OpenSMFS organization**
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Image Super Resolution🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
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Zoom Learn Zoomcomputational zoom from raw sensor data
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Msrn PytorchThis repository is a PyTorch version of the paper "Multi-scale Residual Network for Image Super-Resolution" (ECCV 2018).
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Pytorch LapsrnPytorch implementation for LapSRN (CVPR2017)
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RanksrganICCV 2019 (oral) RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution. PyTorch implementation
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PaddleganPaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, wav2lip, picture repair, image editing, photo2cartoon, image style transfer, and so on.
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cucimNo description or website provided.
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bg-atlasapiA lightweight python module to interact with atlases for systems neuroscience
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WaifuLiteSuper Resolution for Anime image, lightweight implementation
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atomaiDeep and Machine Learning for Microscopy
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MicroscPSF-PyFast and Accurate 3D PSF Computation for Fluorescence Microscopy
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napari-aicsimageioMultiple file format reading directly into napari using pure Python
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tf-perceptual-eusrA TensorFlow-based image super-resolution model considering both quantitative and perceptual quality
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OADCollection of tools and scripts useful to automate microscopy workflows in ZEN Blue using Python and Open Application Development tools and AI tools.
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ColiCoordsSingle-cell fluorescence microscopy data analysis
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micromanager-samplesPython samples for Micro-Manager: image acquisition and microscope control system
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pytorch-gansPyTorch implementation of GANs (Generative Adversarial Networks). DCGAN, Pix2Pix, CycleGAN, SRGAN
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odemisOpen Delmic Microscope Software
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TEGANGenerative Adversarial Network (GAN) for physically realistic enrichment of turbulent flow fields
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DeconvolutionLab2Java (ImageJ/Fiji) software package for 3D deconvolution microscopy
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Deep Iterative CollaborationPytorch implementation of Deep Face Super-Resolution with Iterative Collaboration between Attentive Recovery and Landmark Estimation (CVPR 2020)
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DANThis is an official implementation of Unfolding the Alternating Optimization for Blind Super Resolution
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ThreeDeconv.jlA convex 3D deconvolution algorithm for low photon count fluorescence imaging
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SrganPhoto-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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MSG-NetDepth Map Super-Resolution by Deep Multi-Scale Guidance, ECCV 2016
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Master Thesis BayesiancnnMaster Thesis on Bayesian Convolutional Neural Network using Variational Inference
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LandEverything within the Land model (Soil Plant Atmosphere Module, Land Hydrology, etc)
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Single-Image-Example-Based-Super-ResolutionSingle image example-based super resolution. Improves the spatial and temporal resolution of an image using a direct mapping of LR HR patch pairs. C++, openCV.
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IseebetteriSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press
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IgnnCode repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" (NeurIPS'20)
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eemRUtilities for pre-processing emission-excitation-matrix (EEM).
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Highres NetPytorch implementation of HighRes-net, a neural network for multi-frame super-resolution, trained and tested on the European Space Agency’s Kelvin competition.
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CSSRCrack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution (CSSR) was accepted to international conference on MVA2021 (oral), and selected for the Best Practical Paper Award.
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simple sim fusion demoSimple demo of structured illumination microscopy image fusion via Richardson-Lucy deconvolution
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brainregAutomated 3D brain registration with support for multiple species and atlases.
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LFSSR-SAS-PyTorchRepository for "Light Field Spatial Super-resolution Using Deep Efficient Spatial-Angular Separable Convolution" , TIP 2018
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ESPCN-PyTorchA PyTorch implementation of ESPCN based on CVPR 2016 paper Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network.
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remote refocusA scientific publication, describing a way to improve microscopy. This repository hosts everything you need to reproduce our results. Read the publication here: https://andrewgyork.github.io/remote_refocus/
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