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Benchmark and resources for single super-resolution algorithms

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Super-Resolution.Benckmark

A curated list of super-resolution resources and a benchmark for single image super-resolution algorithms.

See my implementated super-resolution algorithms:

TODO

Build a benckmark like SelfExSR_Code

State-of-the-art algorithms

Classical Sparse Coding Method

  • ScSR [Web]
  • Image super-resolution as sparse representation of raw image patches (CVPR2008), Jianchao Yang et al.
  • Image super-resolution via sparse representation (TIP2010), Jianchao Yang et al.
  • Coupled dictionary training for image super-resolution (TIP2011), Jianchao Yang et al.

Anchored Neighborhood Regression Method

  • ANR [Web]
  • Anchored Neighborhood Regression for Fast Example-Based Super-Resolution (ICCV2013), Radu Timofte et al.
  • A+ [Web]
  • A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution (ACCV2014), Radu Timofte et al.
  • IA [Web]
  • Seven ways to improve example-based single image super resolution (CVPR2016), Radu Timofte et al.

Self-Exemplars

  • SelfExSR [Web]
  • Single Image Super-Resolution from Transformed Self-Exemplars (CVPR2015), Jia-Bin Huang et al.

Bayes

  • NBSRF [Web]
  • Naive Bayes Super-Resolution Forest (ICCV2015), Jordi Salvador et al.

Deep Learning Method

  • SRCNN [Web] [waifu2x by nagadomi]
  • Image Super-Resolution Using Deep Convolutional Networks (ECCV2014), Chao Dong et al.
  • Image Super-Resolution Using Deep Convolutional Networks (TPAMI2015), Chao Dong et al.
  • CSCN [Web]
  • Deep Networks for Image Super-Resolution with Sparse Prior (ICCV2015), Zhaowen Wang et al.
  • Robust Single Image Super-Resolution via Deep Networks with Sparse Prior (TIP2016), Ding Liu et al.
  • VDSR [Web] [Unofficial Implementation in Caffe]
  • Accurate Image Super-Resolution Using Very Deep Convolutional Networks (CVPR2016), Jiwon Kim et al.
  • DRCN [Web]
  • Deeply-Recursive Convolutional Network for Image Super-Resolution (CVPR2016), Jiwon Kim et al.
  • ESPCN [PDF]
  • Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network (CVPR2016), Wenzhe Shi et al.
  • Is the deconvolution layer the same as a convolutional layer? [PDF]
  • Checkerboard artifact free sub-pixel convolution [PDF]
  • FSRCNN [Web]
  • Acclerating the Super-Resolution Convolutional Neural Network (ECCV2016), Dong Chao et al.
  • LapSRN [Web]
  • Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution (CVPR 2017), Wei-Sheng Lai et al.
  • EDSR [PDF]
  • Enhanced Deep Residual Networks for Single Image Super-Resolution (Winner of NTIRE2017 Super-Resolution Challenge), Bee Lim et al.

Perceptual Loss and GAN

  • Perceptual Loss [PDF]
  • Perceptual Losses for Real-Time Style Transfer and Super-Resolution (ECCV2016), Justin Johnson et al.
  • SRGAN [PDF]
  • Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (CVPR2017), Christian Ledig et al.
  • AffGAN [PDF]
  • AMORTISED MAP INFERENCE FOR IMAGE SUPER-RESOLUTION (ICLR2017), Casper Kaae Sønderby et al.
  • EnhanceNet [PDF]
  • EnhanceNet: Single Image Super-Resolution through Automated Texture Synthesis, Mehdi S. M. Sajjadi et al.
  • neural-enchance [Github]

Video SR

  • VESPCN [PDF]
  • Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation (CVPR2017), Jose Caballero et al.

Dicussion

Deconvolution and Sub-Pixel Convolution

Datasets

Test Dataset Image source
Set 5 Bevilacqua et al. BMVC 2012
Set 14 Zeyde et al. LNCS 2010
BSD 100 Martin et al. ICCV 2001
Urban 100 Huang et al. CVPR 2015
Train Dataset Image source
Yang 91 Yang et al. CVPR 2008
BSD 200 Martin et al. ICCV 2001
General 100 Dong et al. ECCV 2016
ImageNet Olga Russakovsky et al. IJCV 2015
COCO Tsung-Yi Lin et al. ECCV 2014

Quantitative comparisons

Results from papers of VDSR, DRCN, CSCN and IA.

Note: IA use enchanced prediction trick to improve result.

Results on Set 5
Scale Bicubic A+ SRCNN SelfExSR CSCN VDSR DRCN IA
2x - PSNR/SSIM 33.66/0.9929 36.54/0.9544 36.66/0.9542 36.49/0.9537 36.93/0.9552 37.53/0.9587 37.63/0.9588 37.39/
3x - PSNR/SSIM 30.39/0.8682 32.59/0.9088 32.75/0.9090 32.58/0.9093 33.10/0.9144 33.66/0.9213 33.82/0.9226 33.46/
4x - PSNR/SSIM 28.42/0.8104 30.28/0.8603 30.48/0.8628 30.31/0.8619 30.86/0.8732 31.35/0.8838 31.53/0.8854 31.10/
Results on Set 14
Scale Bicubic A+ SRCNN SelfExSR CSCN VDSR DRCN IA
2x - PSNR/SSIM 30.24/0.8688 32.28/0.9056 32.42/0.9063 32.22/0.9034 32.56/0.9074 33.03/0.9124 33.04/0.9118 32.87/
3x - PSNR/SSIM 27.55/0.7742 29.13/0.8188 29.28/0.8209 29.16/0.8196 29.41/0.8238 29.77/0.8314 29.76/0.8311 29.69/
4x - PSNR/SSIM 26.00/0.7027 27.32/0.7491 27.49/0.7503 27.40/0.7518 27.64/0.7587 28.01/0.7674 28.02/0.7670 27.88/
Results on BSD 100
Scale Bicubic A+ SRCNN SelfExSR CSCN VDSR DRCN IA
2x - PSNR/SSIM 29.56/0.8431 31.21/0.8863 31.36/0.8879 31.18/0.8855 31.40/0.8884 31.90/0.8960 31.85/0.8942 31.79/
3x - PSNR/SSIM 27.21/0.7385 28.29/0.7835 28.41/0.7863 28.29/0.7840 28.50/0.7885 28.82/0.7976 28.80/0.7963 28.76/
4x - PSNR/SSIM 25.96/0.6675 26.82/0.7087 26.90/0.7101 26.84/0.7106 27.03/0.7161 27.29/0.7251 27.23/0.7233 27.25/

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