Sparse deconvolution_v1.0.3

Words written in the front: Physical resolution might be meaningless if in the mathmetical space.

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).

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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

This repository contains the updating version of Sparse deconvolution. 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.

Instruction

• The binary executable files (.exe/.app) can be found in the release
• More details on Wiki and Document.
• /src_unix is the source code for Unix-like systems (including MacOS).
• /src_win is the source code for Windows systems.
• Clone/download, and run the Install.m
• The input Effective NA should be given according to the sum of illumination NA and detection NA. For instances: wide-field is the objective NA (e.g., 1.49); SIM is the illumination NA + objective NA (e.g., 1.3 + 1.7); SD-SIM is ~1.8 * objective NA.
• Please try help xxx to get the API.

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.

Algorithm UI

• Details in the Wiki and Document.

Parameters: Wiki and Document

Tested platform

This software has been tested on:

• MATLAB R2017b on (Win 10: 128 GB and NVIDIA Titan Xp: 12GB; CUDA 9.1);
• MATLAB R2019b on (Win 10: 128 GB and NVIDIA Titan RTX: 24GB; CUDA 10.0);
• MATLAB R2019b on (Win 10: 16GB and NVIDIA GTX1050Ti: 4GB, CUDA 10.2);
• MATLAB R2015b on (CentOS 7: 64GB and Tesla K40 :12GB, CUDA 9.0);
• MATLAB R2018b on (Ubuntu 18.04: 16GB and NVIDIA TITAN Xp: 12GB, CUDA 10.1);
• MATLAB R2017b on (MacOS 10: 8GB without GPU acceleration).

More on Wiki.

Version

• v1.0.3 Fully open source!
• v1.0.3 Another type deconvolution, and up-sampling methods, first officially released version!
• v0.6.3 Reorder the background estimation
• v0.6.2 Debug mode
• v0.6.1 Progress bar feature and logo
• v0.5.1 Up-sampling feature and change input file type from .mat to .tif
• v0.4.1 Background estimation feature
• v0.3.0 Algorithm UI
• v0.2.0 Full model reconstruction
• v0.1.0 Sparsity reconstruction core

Related links:

• Python version of Sparse deconvolution: sparse-deconv-py
• A light weight MATLAB library for making exsiting images to videos: img2vid
• An adaptive filter to remove isolate hot pixels: Adaptive filter imagej-plugin
• A tool for multi-color 2D or 3D imaging: Merge channels
• Further reading: #behind_the_paper & blog
• Some fancy results and comparisons: my website
• Preprint: Weisong Zhao et al., Extending resolution of structured illumination microscopy with sparse deconvolution, Research Square (2021).
• Reference: Weisong Zhao et al. Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy, Nature Biotechnology 40, 606–617 (2022).

Plans

Debug mode for parameter-adjustment;

A Pyhton version of Sparse deconvolution;

A imagej-plugin of Sparse deconvolution;

A Headless mode;

Reduce the necessary/exposed parameters.

Open source Sparse deconvolution

• This software and corresponding methods can only be used for non-commercial use, and they are under Open Data Commons Open Database License v1.0.
• Feedback, questions, bug reports and patches are welcome and encouraged!