{"id":21802527,"url":"https://github.com/qengineering/real-esrgan-ncnn-raspberry-pi-4","last_synced_at":"2026-03-11T14:34:58.362Z","repository":{"id":112947880,"uuid":"583913740","full_name":"Qengineering/Real-ESRGAN-ncnn-Raspberry-Pi-4","owner":"Qengineering","description":"ESRGAN super resolution with ncnn on Raspberry Pi ","archived":false,"fork":false,"pushed_at":"2022-12-31T17:24:56.000Z","size":34082,"stargazers_count":6,"open_issues_count":1,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-04T13:43:25.531Z","etag":null,"topics":["cpp","deep-learning","esrgan","image-reconstruction","image-restoration","ncnn","ncnn-framework","ncnn-model","raspberry-pi","raspberry-pi-4","raspberry-pi-64-os","super-resolution"],"latest_commit_sha":null,"homepage":"https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Qengineering.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}},"created_at":"2022-12-31T12:41:20.000Z","updated_at":"2024-06-01T16:28:37.000Z","dependencies_parsed_at":null,"dependency_job_id":"9161ee26-5ef2-4f48-85ab-484ee8542d28","html_url":"https://github.com/Qengineering/Real-ESRGAN-ncnn-Raspberry-Pi-4","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Qengineering/Real-ESRGAN-ncnn-Raspberry-Pi-4","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FReal-ESRGAN-ncnn-Raspberry-Pi-4","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FReal-ESRGAN-ncnn-Raspberry-Pi-4/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FReal-ESRGAN-ncnn-Raspberry-Pi-4/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FReal-ESRGAN-ncnn-Raspberry-Pi-4/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Qengineering","download_url":"https://codeload.github.com/Qengineering/Real-ESRGAN-ncnn-Raspberry-Pi-4/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Qengineering%2FReal-ESRGAN-ncnn-Raspberry-Pi-4/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":278862684,"owners_count":26058994,"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":"2025-10-07T02:00:06.786Z","response_time":59,"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":["cpp","deep-learning","esrgan","image-reconstruction","image-restoration","ncnn","ncnn-framework","ncnn-model","raspberry-pi","raspberry-pi-4","raspberry-pi-64-os","super-resolution"],"created_at":"2024-11-27T11:29:18.774Z","updated_at":"2025-10-07T23:30:35.313Z","avatar_url":"https://github.com/Qengineering.png","language":"C++","funding_links":["https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick\u0026hosted_button_id=CPZTM5BB3FCYL"],"categories":[],"sub_categories":[],"readme":"# Real ESRGAN Raspberry Pi 4\n![output image]( https://qengineering.eu/github/ESRGAN_flat.webp )\n## Super-resolution with the ncnn framework. \u003cbr/\u003e\n[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)\u003cbr/\u003e\u003cbr/\u003e\nPaper: https://arxiv.org/pdf/2107.10833.pdf\u003cbr/\u003e\u003cbr/\u003e\nSpecial made for a bare Raspberry Pi 4, see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html)\n\n------------\n\n## Dependencies.\nTo run the application, you have to:\n- A raspberry Pi 4 with a 32 or 64-bit operating system. It can be the Raspberry 64-bit OS, or Ubuntu 18.04 / 20.04. [Install 64-bit OS](https://qengineering.eu/install-raspberry-64-os.html) \u003cbr/\u003e\n- The Tencent ncnn framework installed. [Install ncnn](https://qengineering.eu/install-ncnn-on-raspberry-pi-4.html) \u003cbr/\u003e\n- OpenCV 64 bit installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-raspberry-64-os.html) \u003cbr/\u003e\n- Code::Blocks installed. (```$ sudo apt-get install codeblocks```)\n\n------------\n\n## Installing the app.\nTo extract and run the network in Code::Blocks \u003cbr/\u003e\n$ mkdir *MyDir* \u003cbr/\u003e\n$ cd *MyDir* \u003cbr/\u003e\n$ wget https://github.com/Qengineering/Real-ESRGAN-ncnn-Raspberry-Pi-4/archive/refs/heads/main.zip \u003cbr/\u003e\n$ unzip -j master.zip \u003cbr/\u003e\nRemove master.zip, LICENSE and README.md as they are no longer needed. \u003cbr/\u003e \n$ rm master.zip \u003cbr/\u003e\n$ rm LICENSE \u003cbr/\u003e\n$ rm README.md \u003cbr/\u003e \u003cbr/\u003e\nYour *MyDir* folder must now look like this: \u003cbr/\u003e \n0.png \u003cbr/\u003e\nflat.png \u003cbr/\u003e\ngarden.png \u003cbr/\u003e\nESRGAN.cpb \u003cbr/\u003e\nmain.cpp \u003cbr/\u003e\nrealesrgan.cpp \u003cbr/\u003e\nrealesrgan.h \u003cbr/\u003e\nreal_esrgan.bin \u003cbr/\u003e\nreal_esrgan.param \u003cbr/\u003e\n\n------------\n\n## Running the app.\nTo run the application, load the ESRGAN.cbp project file into Code::Blocks. More information? Follow the instructions at [Hands-On](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html#HandsOn).\u003cbr/\u003e\u003cbr/\u003e\nLarge images can take a VERY long time to process. The photos of the flat and garden took more than 10 minutes on an overclocked Pi.\u003cbr/\u003e\u003cbr/\u003e\nFor best results, do not use jpeg compressed images. Strong jpeg compression generates typical artefacts to which the super-resolution algorithm does not respond well.\u003cbr/\u003e\u003cbr/\u003e\n![output image]( https://qengineering.eu/github/ESRGAN_garden.webp )\n\n------------\n\n[![paypal](https://qengineering.eu/images/TipJarSmall4.png)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick\u0026hosted_button_id=CPZTM5BB3FCYL) \n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Freal-esrgan-ncnn-raspberry-pi-4","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fqengineering%2Freal-esrgan-ncnn-raspberry-pi-4","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fqengineering%2Freal-esrgan-ncnn-raspberry-pi-4/lists"}