{"id":31065786,"url":"https://github.com/henriqueslab/nanoj-esrrf","last_synced_at":"2025-09-15T16:57:22.230Z","repository":{"id":40547093,"uuid":"377531616","full_name":"HenriquesLab/NanoJ-eSRRF","owner":"HenriquesLab","description":null,"archived":false,"fork":false,"pushed_at":"2025-05-30T10:51:27.000Z","size":23501,"stargazers_count":20,"open_issues_count":11,"forks_count":1,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-05-30T13:22:18.512Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Java","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/HenriquesLab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2021-06-16T14:50:07.000Z","updated_at":"2025-05-30T10:51:31.000Z","dependencies_parsed_at":"2025-05-30T11:32:04.218Z","dependency_job_id":"3ffc8a46-eef1-41de-9d0a-287257aa00b0","html_url":"https://github.com/HenriquesLab/NanoJ-eSRRF","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/HenriquesLab/NanoJ-eSRRF","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HenriquesLab%2FNanoJ-eSRRF","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HenriquesLab%2FNanoJ-eSRRF/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HenriquesLab%2FNanoJ-eSRRF/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HenriquesLab%2FNanoJ-eSRRF/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/HenriquesLab","download_url":"https://codeload.github.com/HenriquesLab/NanoJ-eSRRF/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/HenriquesLab%2FNanoJ-eSRRF/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":275289704,"owners_count":25438494,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-15T02:00:09.272Z","response_time":75,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-09-15T16:57:19.563Z","updated_at":"2025-09-15T16:57:22.221Z","avatar_url":"https://github.com/HenriquesLab.png","language":"Java","funding_links":[],"categories":[],"sub_categories":[],"readme":"# NanoJ-eSRRF\n\n## Adaptive image reconstruction for high-fidelity, fast and easy-to-use 3D live-cell super-resolution microscopy\n\neSRRF (enhanced Super-Resolution Radial Fluctuations) is an extension of the SRRF method developed by the Henriques lab, described in **[Laine \u0026 Heil _et al._ (2023)](https://www.nature.com/articles/s41592-023-02057-w)**. For more details you can also check out the **[preprint](https://doi.org/10.1101/2022.04.07.487490)** on bioRXiv, or the publicaton on SRRF: **[Gustafsson _et al._ (2016)](http://www.nature.com/articles/ncomms12471)**. \n\neSRRF aims at improving the fidelity of SRRF images with respect to the underlying true structure. Below is shown a representative dataset obtained from high-density emitters for which the underlying structure was obtained via DNA-PAINT (SMLM). \n\n\n\u003cimg src=\"https://github.com/HenriquesLab/NanoJ-eSRRF/blob/master/wiki_files/eSRRF_promo.gif\" width=\"500\"/\u003e\n\nThe (e)SRRF approach is based on\n* **A spatial analysis** of the high-density emitter data using Radial symmetry transform;\n* **A temporal analysis** of the obtained temporal stack using similar appraoch to SOFI.\n\n\u003cimg src=\"https://github.com/HenriquesLab/NanoJ-eSRRF/blob/master/wiki_files/eSRRF_method.png\" width=\"500\"/\u003e\n\n## Features of eSRRF\n\nSome of the new features available in eSRRF include:\n* Improved fidelity of reconstructions;\n* Adaptive reconstruction schemes allowing to explore the compromise between **fidelity** and **resolution**. This is enabled by an integration of our SQUIRREL approach, described in **[Culley _et al._ (2018)](https://doi.org/10.1038/nmeth.4605)**;\n* Estimation of the maximum number of frames to use for eSRRF analysis from a dataset, based on SSIM (or optical flow magnitude) calculation;\n* Rolling analysis;\n* Integrated drift correction;\n* Better memory management;\n* Full OpenCL integration, enabling GPU acceleration;\n* Direct saving to disk for large dataset analysis;\n* **eSRRF 3D** reconstruction!\n\n## Getting the eSRRF plugin on Fiji\n\nThe latest stable version of eSRRF can be directly obtained from our Fiji update site: **https://sites.imagej.net/NanoJ-LiveSRRF/**\n\nInformation about update sites can be found [here](https://imagej.net/update-sites/).\n\nVideo guide: Installation\n\n[\u003cimg alt=\"Video guide: Installation\" width=\"400px\" src=\"https://github.com/HenriquesLab/NanoJ-eSRRF/blob/master/wiki_files/eSRRF-Guide%20installation_screeshotP.png\" /\u003e](https://www.youtube.com/watch?v=3Oa2ADEa-qY)\n\nThere have been some issues reported with OpenCl and running NanoJ-Squirrel and NanoJ-eSRRF on Windows10/11. You can find an instruction with the temporal fix in the [wiki](https://github.com/HenriquesLab/NanoJ-eSRRF/wiki#nanoj-esrrf-opencl-issues).\n\n:sparkles: :sparkles: Update November 2023 :sparkles: :sparkles:: \n\neSRRF is now also available in **Python** :snake:! You'll find the code and notebooks here: :point_right: [https://github.com/HenriquesLab/NanoPyx](https://github.com/HenriquesLab/NanoPyx)\n\n:sparkles: :sparkles: :sparkles: :sparkles: :sparkles: :sparkles: :sparkles: :sparkles: :sparkles: :sparkles: :sparkles: :sparkles:\n\n## Test datasets\nWe have published test datasets including eSRRF parameter suggestions on [Zenodo](https://doi.org/10.5281/zenodo.6466472). Download and get started right away!\n\nVideo guide: Getting started\n\n[\u003cimg alt=\"Video guide: Getting started\" width=\"400px\" src=\"https://github.com/HenriquesLab/NanoJ-eSRRF/blob/master/wiki_files/eSRRF-Guide getting started_screeshotP.png\" /\u003e](https://www.youtube.com/watch?v=VScGXvdKoNg))\n\n\n## Tools included in the eSRRF plugin\n\neSRRF comes packed with useful Tools plugins to perform a range of things, such as (but not limited to):\n* Fluorescence fluctuation simulator;\n* Rescale individual slices within a stack and convert it to RGB (useful to visualise the parameter sweep output);\n* Save all current open images as Tiff files;\n* Perform linear rescaling on stack;\n\n## People involved\n\nMany people are involved in developing and testing this method, here are some of the key players:\n* Romain F. Laine ([@LaineBioImaging](https://twitter.com/LaineBioImaging))\n* Ricardo Henriques ([@HenriquesLab](https://twitter.com/HenriquesLab))\n* Guillaume Jacquemet ([@guijacquemet](https://twitter.com/guijacquemet))\n* Christophe Leterrier ([@christlet](https://twitter.com/christlet))\n* Siân Culley ([@SuperResoluSian](https://twitter.com/SuperResoluSian))\n* Bassam Hajj ([@Bassam_A_HAJJ](https://twitter.com/Bassam_A_HAJJ))\n* Hannah S. Heil ([@Hannah_SuperRes](https://twitter.com/hannah_superres))\n* Simao Coelho ([@simaopc](https://twitter.com/simaopc))\n* Jonathon Nixon-Abell ([@AbellJonny](https://twitter.com/AbellJonny))\n* Angélique Jimenez \n* Tommaso Galgani\n* Aki Stubb ([@akistub](https://twitter.com/akistub))\n* Gautier Follain ([@Follain_Ga](https://twitter.com/Follain_Ga))\n* Samantha Webster\n* Jesse Goyette\n\n## eSRRFing in Python\n\nExciting news! enhanced Super-Resolution Radial Fluctuations (eSRRF) is now accessible in Python through the [NanoPyx](https://github.com/HenriquesLab/NanoPyx) package. This integration brings the power and versatility of eSRRF to Python users, opening up new possibilities for analysis and integration within Python-based workflows.\n\nNanoPyx seamlessly integrates eSRRF capabilities into Python environments. With NanoPyx, users can now leverage eSRRF's high-performance analytical approach within their Python scripts, pipelines, and interactive sessions. Through NanoPyx, eSRRF is also available as \"codeless\" Jupyter Notebooks and a [napari plugin](https://github.com/HenriquesLab/napari-NanoPyx).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhenriqueslab%2Fnanoj-esrrf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhenriqueslab%2Fnanoj-esrrf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhenriqueslab%2Fnanoj-esrrf/lists"}