Paper: https://arxiv.org/pdf/2107.10833.pdf

Special made for a bare Raspberry Pi 4, see Q-engineering deep learning examples

To run the application, you have to:

• 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
  
• The Tencent ncnn framework installed. Install ncnn
  
• OpenCV 64 bit installed. Install OpenCV 4.5
  
• Code::Blocks installed. ($ sudo apt-get install codeblocks)

To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/Real-ESRGAN-ncnn-Raspberry-Pi-4/archive/refs/heads/main.zip
$ unzip -j master.zip
Remove master.zip, LICENSE and README.md as they are no longer needed.
$ rm master.zip
$ rm LICENSE
$ rm README.md

Your MyDir folder must now look like this:
0.png
flat.png
garden.png
ESRGAN.cpb
main.cpp
realesrgan.cpp
realesrgan.h
real_esrgan.bin
real_esrgan.param

To run the application, load the ESRGAN.cbp project file into Code::Blocks. More information? Follow the instructions at Hands-On.

Large 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.

For best results, do not use jpeg compressed images. Strong jpeg compression generates typical artefacts to which the super-resolution algorithm does not respond well.