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A PyTorch implementation of SRGAN based on CVPR 2017 paper "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"
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README.md

SRGAN

A PyTorch implementation of SRGAN based on CVPR 2017 paper Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network.

Requirements

conda install pytorch torchvision -c pytorch
  • opencv
conda install opencv

Datasets

Train、Val Dataset

The train and val datasets are sampled from VOC2012. Train dataset has 16700 images and Val dataset has 425 images. Download the datasets from here(access code:5tzp), and then extract it into data directory.

Test Image Dataset

The test image dataset are sampled from | Set 5 | Bevilacqua et al. BMVC 2012 | Set 14 | Zeyde et al. LNCS 2010 | BSD 100 | Martin et al. ICCV 2001 | Sun-Hays 80 | Sun and Hays ICCP 2012 | Urban 100 | Huang et al. CVPR 2015. Download the image dataset from here(access code:xwhy), and then extract it into data directory.

Test Video Dataset

The test video dataset are three trailers. Download the video dataset from here(access code:zabi).

Usage

Train

python train.py

optional arguments:
--crop_size                   training images crop size [default value is 88]
--upscale_factor              super resolution upscale factor [default value is 4](choices:[2, 4, 8])
--num_epochs                  train epoch number [default value is 100]

The output val super resolution images are on training_results directory.

Test Benchmark Datasets

python test_benchmark.py

optional arguments:
--upscale_factor              super resolution upscale factor [default value is 4]
--model_name                  generator model epoch name [default value is netG_epoch_4_100.pth]

The output super resolution images are on benchmark_results directory.

Test Single Image

python test_image.py

optional arguments:
--upscale_factor              super resolution upscale factor [default value is 4]
--test_mode                   using GPU or CPU [default value is 'GPU'](choices:['GPU', 'CPU'])
--image_name                  test low resolution image name
--model_name                  generator model epoch name [default value is netG_epoch_4_100.pth]

The output super resolution image are on the same directory.

Test Single Video

python test_video.py

optional arguments:
--upscale_factor              super resolution upscale factor [default value is 4]
--video_name                  test low resolution video name
--model_name                  generator model epoch name [default value is netG_epoch_4_100.pth]

The output super resolution video and compared video are on the same directory.

Benchmarks

Upscale Factor = 2

Epochs with batch size of 64 takes ~2 minute 30 seconds on a NVIDIA GTX 1080Ti GPU.

Image Results

The left is bicubic interpolation image, the middle is high resolution image, and the right is super resolution image(output of the SRGAN).

  • BSD100_070(PSNR:32.4517; SSIM:0.9191)

BSD100_070

  • Set14_005(PSNR:26.9171; SSIM:0.9119)

Set14_005

  • Set14_013(PSNR:30.8040; SSIM:0.9651)

Set14_013

  • Urban100_098(PSNR:24.3765; SSIM:0.7855)

Urban100_098

Video Results

The left is bicubic interpolation video, the right is super resolution video(output of the SRGAN).

Watch the video

Upscale Factor = 4

Epochs with batch size of 64 takes ~4 minute 30 seconds on a NVIDIA GTX 1080Ti GPU.

Image Results

The left is bicubic interpolation image, the middle is high resolution image, and the right is super resolution image(output of the SRGAN).

  • BSD100_035(PSNR:32.3980; SSIM:0.8512)

BSD100_035

  • Set14_011(PSNR:29.5944; SSIM:0.9044)

Set14_011

  • Set14_014(PSNR:25.1299; SSIM:0.7406)

Set14_014

  • Urban100_060(PSNR:20.7129; SSIM:0.5263)

Urban100_060

Video Results

The left is bicubic interpolation video, the right is super resolution video(output of the SRGAN).

Watch the video

Upscale Factor = 8

Epochs with batch size of 64 takes ~3 minute 30 seconds on a NVIDIA GTX 1080Ti GPU.

Image Results

The left is bicubic interpolation image, the middle is high resolution image, and the right is super resolution image(output of the SRGAN).

  • SunHays80_027(PSNR:29.4941; SSIM:0.8082)

SunHays80_027

  • SunHays80_035(PSNR:32.1546; SSIM:0.8449)

SunHays80_035

  • SunHays80_043(PSNR:30.9716; SSIM:0.8789)

SunHays80_043

  • SunHays80_078(PSNR:31.9351; SSIM:0.8381)

SunHays80_078

Video Results

The left is bicubic interpolation video, the right is super resolution video(output of the SRGAN).

Watch the video

The complete test results could be downloaded from here(access code:wnvn).

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