ncnn implementation of Real-World Super-Resolution via Kernel Estimation and Noise Injection super resolution.
realsr-ncnn-vulkan uses ncnn project as the universal neural network inference framework.
Real-World Super-Resolution via Kernel Estimation and Noise Injection (CVPRW 2020)
https://github.com/jixiaozhong/RealSR
Xiaozhong Ji, Yun Cao, Ying Tai, Chengjie Wang, Jilin Li, and Feiyue Huang
Tencent YouTu Lab
Our solution is the winner of CVPR NTIRE 2020 Challenge on Real-World Super-Resolution in both tracks.
https://arxiv.org/abs/2005.01996
realsr-ncnn-vulkan.exe -i input.jpg -o output.png -s 4
Usage: realsr-ncnn-vulkan -i infile -o outfile [options]...
-h show this help
-v verbose output
-i input-path input image path (jpg/png) or directory
-o output-path output image path (png) or directory
-s scale upscale ratio (4, default=4)
-t tile-size tile size (>=32/0=auto, default=0)
-m model-path realsr model path (default=models-DF2K_JPEG)
-g gpu-id gpu device to use (default=0)
-j load:proc:save thread count for load/proc/save (default=1:2:2)
-x enable tta mode
input-path
andoutput-path
accept either file path or directory pathscale
= scale level, 4=upscale 4xtile-size
= tile size, use smaller value to reduce GPU memory usage, default is 400load:proc:save
= thread count for the three stages (image decoding + realsr upscaling + image encoding), use larger value may increase GPU utility and consume more GPU memory. You can tune this configuration as "4:4:4" for many small-size images, and "2:2:2" for large-size images. The default setting usually works fine for most situations. If you find that your GPU is hungry, do increase thread count to achieve faster processing.
If you encounter crash or error, try to upgrade your GPU driver
- Intel: https://downloadcenter.intel.com/product/80939/Graphics-Drivers
- AMD: https://www.amd.com/en/support
- NVIDIA: https://www.nvidia.com/Download/index.aspx
convert origin.jpg -resize 400% output.png
srmd-ncnn-vulkan.exe -i origin.jpg -o 4x.png -s 4 -n -1
realsr-ncnn-vulkan.exe -i origin.jpg -o output.png -s 4 -x -m models-DF2K
- https://github.com/Tencent/ncnn for fast neural network inference on ALL PLATFORMS
- https://github.com/nothings/stb for decoding and encoding image on Linux / MacOS
- https://github.com/tronkko/dirent for listing files in directory on Windows