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.
Classification-Based
- Local Patch Classification [Web]
- Local Patch Classification Based Framework for Single Image Super-Resolution (Arxiv2017), Yang Zhao 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]
- FSRCNN [Web]
- Acclerating the Super-Resolution Convolutional Neural Network (ECCV2016), Dong Chao et al.
- GNU [Web]
- GUN: Gradual Upsampling Network for single image super-resolution (Arxiv2017), Yang Zhao et al.
- Model Adaptation [Web]
- Single Image Super Resolution - When Model Adaptation Matters (Arxiv2017), Yudong Liang et al.
- Autoencoding Priors [Web]
- Image Restoration using Autoencoding Priors (Arxiv2017), Siavash Arjomand Bigdeli and Matthias Zwicker.
- E-ResNet[Web]
- Single Image Super-resolution with a Parameter Economic Residual-like Convolutional Neural Network (Arxiv2017), Yudong Liang et al.
- Mixture [Web]
- Learning a Mixture of Deep Networks for Single Image Super-Resolution (Arxiv2017), Ding Liu et al.
- Pixel Recurrence [Web]
- Pixel Recursive Super Resolution (Arxiv2017), Ryan Dahl et al.
- Deep Laplacian Pyramid Networks [Web][PDF]
- Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution (CVPR 2017), Weisheng Lai et al.
Perceptual Loss and GAN
- Perceptual Loss [PDF]
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution (ECCV2016), Justin Johnson et al.
- SR-ResNet\SRGAN [PDF]
- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, Christian Ledig et al.
- AffGAN [PDF]
- AMORTISED MAP INFERENCE FOR IMAGE SUPER-RESOLUTION, Casper Kaae Sønderby et al.
Texture Synthesis
- ENet [PDF]
- EnhanceNet: Single Image Super-Resolution through Automated Texture Synthesis, Mehdi S. M. Sajjadi et al.
- Neural-Enchance [Github]
- Texture Enhancement [Web]
- Texture Enhancement via High-Resolution Style Transfer for Single-Image Super-Resolution (Arxiv2016), Youngjung Kim et al.
- Constrained Texture Synthesis[Web]
- Super-resolution Using Constrained Deep Texture Synthesis (Arxiv2017), Libin Sun et al.
Others
- Self-Optimizing Mask [Web]
- Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction (Arxiv 2017), Qi Yang 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 |
---|---|---|---|---|---|---|---|
2x | 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 |
3x | 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 |
4x | 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 |
Scale | IA | E-ResNet | Mixture | ENet-E | SR-ResNet |
---|---|---|---|---|---|
2x | 37.39/ | 37.51/0.9587 | 37.16/0.9565 | / | / |
3x | 33.46/ | 33.72/0.9215 | 33.33/0.9155 | / | |
4x | 31.10/ | 31.37/0.8838 | 31.08/0.8740 | 31.74/ | 32.05/0.9019 |
Results on Set 14
Scale | Bicubic | A+ | SRCNN | SelfExSR | CSCN | VDSR | DRCN |
---|---|---|---|---|---|---|---|
2x | 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 |
3x | 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 |
4x | 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 |
Scale | IA | E-ResNet | Mixture | ENet-E | GUN | SR-ResNet |
---|---|---|---|---|---|---|
2x | 32.87/ | 33.10/0.9131 | 32.85/0.9084 | / | 33.35/0.9698 | / |
3x | 29.69/ | 29.80/0.8317 | 29.65/0.8272 | / | 30.08/0.9112 | / |
4x | 27.88/ | 28.06/0.7681 | 27.87/0.7624 | 28.42/ | 28.29/0.8648 | 28.49 / 0.8184 |
Results on BSD 100
Scale | Bicubic | A+ | SRCNN | SelfExSR | CSCN | VDSR | DRCN |
---|---|---|---|---|---|---|---|
2x | 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 |
3x | 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 |
4x | 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 |
Scale | IA | E-ResNet | Mixture | ENet-E | GUN | SR-ResNet |
---|---|---|---|---|---|---|
2x | 31.79/ | 31.91/0.8961 | 31.65/0.8928 | / | 31.49/0.8889 | / |
3x | 28.76/ | 28.83/0.7980 | 28.66/0.7941 | / | 28.49/0.7886 | / |
4x | 27.25/ | 27.27/0.7248 | 27.19/0.7229 | 27.50/ | 27.15/0.7191 | 27.58/0.7620 |