README.md

🔧 Dependencies and Installation

• Python >= 3.7 (Recommend to use Anaconda or Miniconda)
• PyTorch >= 1.7

Installation

1. Clone repo

   git clone https://github.com/xinntao/Real-ESRGAN.git
   cd Real-ESRGAN

2. Install dependent packages

   # Install basicsr - https://github.com/xinntao/BasicSR
   # We use BasicSR for both training and inference
   pip install basicsr
   # facexlib and gfpgan are for face enhancement
   pip install facexlib
   pip install gfpgan
   pip install -r requirements.txt
   python setup.py develop

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Inference

Python script

Usage of python script

1. You can use X4 model for arbitrary output size with the argument outscale. The program will further perform cheap resize operation after the Real-ESRGAN output.

Usage: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile -o outfile [options]...

A common command: python inference_realesrgan.py -n RealESRGAN_x4plus -i infile --outscale 3.5 --face_enhance

  -h                   show this help
  -i --input           Input image or folder. Default: inputs
  -o --output          Output folder. Default: results
  -n --model_name      Model name. Default: RealESRGAN_x4plus
  -s, --outscale       The final upsampling scale of the image. Default: 4
  --suffix             Suffix of the restored image. Default: out
  -t, --tile           Tile size, 0 for no tile during testing. Default: 0
  --face_enhance       Whether to use GFPGAN to enhance face. Default: False
  --fp32               Use fp32 precision during inference. Default: fp16 (half precision).
  --ext                Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto

Inference on Single Image

Download pre-trained models: RealESRGAN_x4plus.pth

wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P weights

Inference

python inference_realesrgan.py -n RealESRGAN_x4plus -i <path to image> --face_enhance 1

Results are in the results folder

Inference on Dataset

Download pre-trained models: RealESRGAN_x4plus.pth

wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P weights

Inference

python dataset_inference.py --dir <dataset_folder_name>

COLMAP Dense Reconstruction

python colmap_runner.py --path <name of folder containing images> --project_name <name of associaed colmap project>
python visualize_mesh_and_pointcloud.py --path <name of associaed colmap project>

Case Results

Case	Ref Image	Pretrained ESRGAN Predictions
1
2
3
4
5
6

Case	Ref Dataset	Pretrained ESRGAN Predictions
1	Synthetic Motion Blurred	Prediction
2	Synthetic Resolution Blurred	Prediction

Ref Dataset COLMAP Reconstruction

colmap_dataset_motion_blurred_video.mp4

Pretrained ESRGAN Predictions COLMAP Reconstruction

colmap_dataset_motion_blurred_out_video.mp4

Ref Dataset COLMAP Reconstruction

colmap_dataset_resolution_blurred_video.mp4

Pretrained ESRGAN Predictions COLMAP Reconstruction

colmap_dataset_resolution_blurred_out_video.mp4

Reference

https://github.com/xinntao/Real-ESRGAN