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:

• SRGAN
• VDSR
• CSCN

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

• Deconvolution and Checkerboard Artifacts
• SubPixel

Datasets

Quantitative comparisons

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

Note: IA use enchanced prediction trick to improve result.

Results on Set 5

Results on Set 14

Results on BSD 100