Home

Awesome

<!-- <p align='left'> <a href="https://weisongzhao.github.io/Sparse-SIM/"><img src='https://img.shields.io/badge/Projects-1.0.3-brightgreen.svg'/> </a> <a href="https://github.com/WeisongZhao/Sparse-SIM/"><img src='https://img.shields.io/badge/code-1.0.3-yellow.svg'/> </a> <a href="https://weisongzhao.github.io/Sparse-SIM/"><img src='https://img.shields.io/badge/website-Up-green.svg' /></a> <a href="https://github.com/WeisongZhao/Sparse-SIM/releases/tag/v1.0.3/"><img src='https://img.shields.io/badge/Release-v1.0.3-blue.svg'/></a> <a href="https://github.com/WeisongZhao/Sparse-SIM/blob/master/LICENSE/"><img src='https://img.shields.io/github/license/WeisongZhao/Sparse-SIM' /></a> <a href="https://www.nature.com/nbt/"><img src='https://img.shields.io/badge/paper-Nature%20Biotechnology-black.svg' /></a> </p> -->

code website releases paper paper<br> Github commit DOI Github All Releases License<br> Twitter GitHub watchers GitHub stars GitHub forks

<p> <h1 align="center">Sparse deconvolution<sub>v1.0.3</sub></h1> <!-- <h6 align="center"><sup>v1.0.3</sup></h6> --> <!-- <h4 align="center">This repository contains the updating version of Sparse deconvolution.</h4> --> </p> <p align='center'> <i>Words written in the front: Physical resolution might be meaningless if in the mathmetical space.</i> </p>

It is a part of publication. For details, please refer to: Weisong Zhao et al. Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy, Nature Biotechnology 40, 606–617 (2022).<hr>

The related Python version can be found at HERE

You can also find some fancy results and comparisons on my website.

If you are interested in our work, I wrote a #behind_the_paper post for further reading.

Here is also a blog about it for further reading.

This method has been tested on various types of Confocal microscopy & STED microscopy, Wide-field & TIRF microscopy, Light-sheet microscopy, Multi-photon microscopy, and Structured illumination microscopy, feasible for single-slice, time-lapse, and volumetric datasets.

Introduction

<b>This repository contains the updating version of Sparse deconvolution.</b> The Sparse deconvolution is an universal post-processing framework for fluorescence (or intensity-based) image restoration, including xy (2D), xy-t (2D along t axis), and xy-z (3D) images. It is based on the natural priori knowledge of forward fluorescence imaging model: sparsity and continuity along xy-t (z) axes.

<p align="center"> <img src='./sources/GUIv2.png' width=750> </p>

Instruction

help SparseHessian_core
help background_estimation
help Fourier_Oversample

Installation of binary executable file (.exe) for Win10 system.

Or directly click the .\for Maltab users\Sparse_SIM.exe if you are using MATLAB 2017b.

<p align='center'> <img src='./sources/SSIM.gif' width='800'/> </p>

Algorithm UI

<p align="center"> <img src='./sources/GUI.png' width=800> </p>

Parameters: Wiki and Document

Tested platform

This software has been tested on:

More on Wiki.

Version

Related links:

<details> <summary><b>Plans</b></summary> <li> <s>Debug mode for parameter-adjustment;</s></li> <li> <s>A Pyhton version of Sparse deconvolution;</s></li> <li> A imagej-plugin of Sparse deconvolution;</li> <li> A Headless mode;</li> <li> Reduce the necessary/exposed parameters.</li> </details>

Open source Sparse deconvolution

<!-- <p align='center'> <img src='./sources/HIT.jpg' width='240'/> <img src='./sources/PKU.jpg' width='240'/> </p> -->