ecolstat's repositories

Image_denoising

复现论文《Salt-and-Pepper Noise Removal by Median-Type Noise Detectors and Detail-Preserving Regularization》

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VegET

VegET: daily ET modeling

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cedgan-interpolation

Demo code for the paper -- Spatial interpolation using conditional generative adversarial neural networks

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CellProfiler

An open-source application for biological image analysis

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codex_dashboard

Single Cell Multiplexed Imaging Jupyter Voila Dashboard using CODEX Data

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cytokit

Microscopy Image Cytometry Toolkit

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dash-canvas

An interactive image editing component for Dash

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DashDataFrame

An simplified interface for making dash apps to explore multi-dimensional dataframes with custom link integration and filtering.

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deep-hs-prior

Single Hyperspectral Image Denoising, Inpainting, Super-Resolution

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easy_dash

A library for making Dash apps easier to build and particularly focusing on common data collection use cases

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EMBL-CodingClub

This repo collects materials for the EMBL Coding Club.

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gc_ndvi_anoms_app_1

Dash app for mapping GrassCast and NDVI anomalies

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machine-learning-course

:speech_balloon: Machine Learning Course with Python

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materials

Bonus materials, exercises, and example projects for our Python tutorials

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MIBIsualization

Visualization tools for MIBItiff data.

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microscopium

Unsupervised clustering of high content screen samples

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NAMF

This project is for our paper "NAMF: A NON-LOCAL ADAPTIVE MEAN FILTER FOR SALT-AND-PEPPER NOISE REMOVAL"

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noise2self

A framework for blind denoising with self-supervision.

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noise_removal

Implementação em python do método descrito no artigo "A New Adaptive Weighted Mean Filter for Removing Salt-and-Pepper Noise"

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PhenoGraph

Subpopulation detection in high-dimensional single-cell data

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phenology_library

NDVI signatures phenology library

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pycroscopy

Scientific analysis of nanoscale materials imaging data

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PythonDataScienceHandbook

Python Data Science Handbook: full text in Jupyter Notebooks

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pytorch_tutorial

PyTorch 1.0 Tutorial

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satellite-image-deep-learning

Resources for performing deep learning on satellite imagery

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segmentation

Segmentation pipeline + quantification

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ssi-code

Image Deconvolution via Noise-Tolerant Self-Supervised Inversion

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trefide

Trend Filter Denoising

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