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Single Image Super Resolution Overview

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LEE HOSEONG
LEE HOSEONG

Overview of SISR(Single Image Super Resolution) using Deep Learning.

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Intelligence Machine Vision Lab Strictly Confidential Single Image Super Resolution Overview SUALAB Ho Seong Lee
2Type A-3 Contents • What is Super Resolution? • Applications of Super Resolution • Deep Learning for Single Image Super Resolution • Some Issues for Super Resolution
3Type A-3 What is Super Resolution? • Super Resolution • Restore High-Resolution(HR) image(or video) from Low-Resolution(LR) image(or video) • According to the number of input LR images, SR can be classified SISR or MISR • Single Image Super Resolution Efficient & Popular Super Resolution
4Type A-3 What is Super Resolution? • Single Image Super Resolution • Restore High-Resolution(HR) image(or video) from Low-Resolution(LR) image(or video) • Ill-Posed Problem.. (Regular Inverse Problem) → We can’t have ground truth from LR image HR 3 HR 2 HR 1 LR
5Type A-3 What is Super Resolution? • Interpolation-based Single Image Super Resolution • In image upscaling task, bicubic or bilinear or Lanczos interpolation is usually used. • Fast, easy.. but low quality.. Super Resolution Deep SR bilinear
6Type A-3 What is Super Resolution? • Single Image Super Resolution algorithms • Interpolation-based method • Reconstruction-based method • (Deep) Learning-based method • Today, I will cover learning-based method
7Type A-3 Applications of Super Resolution • Satellite image processing • Medical image processing • Multimedia Industry and Video Enhancement Reference: “Super Resolution Applications in Modern Digital Image Processing”, 2016 IJCA TV & Monitor HD(1280x720), FHD(1920x180) UHD(3840x2160)
8Type A-3 Deep Learning for Single Image Super Resolution • Learning-based Single Image Super Resolution • For tackling regular inverse problem, almost use this paradigm • [HR(GT) image] + [distortion & down-sampling] → [LR(input) image] • This is limitation of SISR training • Overall restoration quality is dependent on the distortion & down-sampling method Reference: “Deep Learning for Single Image Super-Resolution: A Brief Review”, 2018 IEEE Transactions on Multimedia (TMM)
9Type A-3 Deep Learning for Single Image Super Resolution • Learning-based Single Image Super Resolution • [HR(GT) image] + [distortion & down-sampling] → [LR(input) image] • In CVPR 2017 SR Challenge, many team showed many degradation of quality metric Reference: http://www.vision.ee.ethz.ch/~timofter/publications/NTIRE2017SRchallenge_factsheets.pdf
10Type A-3 Deep Learning for Single Image Super Resolution • First Deep Learning architecture for Single Image Super Resolution • SRCNN(2014) – three-layer CNN, MSE Loss, Early upsampling • Compared to traditional methods, it shows excellent performance. Reference: “Image Super-Resolution Using Deep Convolutional Networks”, 2014 ECCV
11Type A-3 Deep Learning for Single Image Super Resolution • Efficient Single Image Super Resolution • FSRCNN(2016), ESPCN(2016) • Use Late Upsampling with deconvolution or sub-pixel convolutional layer Inefficient in Memory, FLOPS Reference: “Image Super-Resolution Using Deep Convolutional Networks”, 2014 ECCV
12Type A-3 Deep Learning for Single Image Super Resolution • FSRCNN(Fast Super-Resolution Convolutional Neural Network) • Use Deconvolution layer instead of pre-processing(upsampling) • Faster and more accurate than SRCNN Reference: “Accelerating the Super-Resolution Convolutional Neural Network”, 2016 ECCV
13Type A-3 Deep Learning for Single Image Super Resolution • ESPCN(Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network) • Use sub-pixel convolutional layer (pixel shuffler or depth_to_space) • This sub-pixel convolutional layer is used in recent SR models Reference: “Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network”, 2016 CVPR Code: https://github.com/leftthomas/ESPCN
14Type A-3 Deep Learning for Single Image Super Resolution • Deeper Networks for Super-Resolution • SRCNN, FSRCNN, ESPCN are shallow network → Why not deep network? • Failed to train deeper models.. → Use shallow network → how to use deeper network? Reference: “Image Super-Resolution Using Deep Convolutional Networks”, 2014 ECCV
15Type A-3 Deep Learning for Single Image Super Resolution • VDSR(Accurate Image Super-Resolution Using Very Deep Convolutional Networks) • VGG based deeper model(20-layer) for Super-Resolution → large receptive field • Residual learning & High learning rate with gradient clipping • MSE Loss, Early upsampling Reference: “Accurate Image Super-Resolution Using Very Deep Convolutional Networks”, 2016 CVPR
16Type A-3 Deep Learning for Single Image Super Resolution • Deeper Networks for Super-Resolution after VDSR • DRCN(Deeply-recursive Convolutional network), 2016 CVPR • SRResNet, 2017 CVPR • DRRN(Deep Recursive Residual Network), 2017 CVPR Reference: “Deep Learning for Single Image Super-Resolution: A Brief Review”, 2018 IEEE Transactions on Multimedia (TMM)
17Type A-3 Deep Learning for Single Image Super Resolution • Deeper Networks for Super-Resolution after VDSR • EDSR, MDSR (Enhanced Deep Residual Network, Multi Scale EDSR), 2017 CVPRW • DenseSR, 2017 CVPR • MemNet, 2017 CVPR Reference: “Deep Learning for Single Image Super-Resolution: A Brief Review”, 2018 IEEE Transactions on Multimedia (TMM)
18Type A-3 Deep Learning for Single Image Super Resolution • Generative Adversarial Network(GAN) for Super-Resolution • SRGAN(Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network) • First GAN-based SR Model, MSE Loss → Blurry Output → GAN loss + Content loss = Perceptual loss Reference: “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network”, 2017 CVPR
19Type A-3 Deep Learning for Single Image Super Resolution • Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network • MSE Loss → Blurry Output → GAN loss + Content loss = Perceptual loss • Replace MSE loss to VGG loss (used in style transfer) and add adversarial loss Reference: “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network”, 2017 CVPR
20Type A-3 Deep Learning for Single Image Super Resolution • Generative Adversarial Network(GAN) for Super-Resolution • SRGAN, EnhanceNet, SRFeat, ESRGAN Reference: “A Deep Journey into Super-resolution: A survey”, 2019 arXiv
21Type A-3 Deep Learning for Single Image Super Resolution Reference: “A Deep Journey into Super-resolution: A survey”, 2019 arXiv • Deep Learning for Single Image Super Resolution
22Type A-3 Deep Learning for Single Image Super Resolution Reference: “A Deep Journey into Super-resolution: A survey”, 2019 arXiv
23Type A-3 Deep Learning for Single Image Super Resolution • Deep Learning for Single Image Super Resolution
24Type A-3 Some Issues for Super Resolution • Checkerboard artifact • Deconvolution (Transposed convolution) layer can easily have “uneven overlap” • Simple solution: use “resize + conv” or “sub-pixel convolutional layer” Reference: “Deconvolution and Checkerboard Artifacts”, distill blog(https://distill.pub/2016/deconv-checkerboard/)
25Type A-3 Some Issues for Super Resolution • Loss function • Propose a various loss function methods in Image Restoration task • Report the best result when using mixed loss with MS-SSIM loss + 𝒍 𝟏 loss Reference: “Loss Functions for Image Restoration with Neural Networks”, 2016 IEEE TCI
26Type A-3 Some Issues for Super Resolution • Loss function • Recent papers almost use 𝒍 𝟏 loss Reference: “A Deep Journey into Super-resolution: A survey”, 2019 arXiv
27Type A-3 Some Issues for Super Resolution • Metric (Distortion measure) • Almost paper use distortion metric(PSNR and SSIM) as performance metric • But, high PSNR, SSIM do not guarantee good quality that looks good to human.. Reference: “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network”, 2017 CVPR
28Type A-3 Some Issues for Super Resolution • Metric (Human opinion score) • But, high PSNR, SSIM do not guarantee good quality that looks good to human.. • In SRGAN Paper, Mean Opinion Score(MOS) Testing is done for quantify perceptual quality • 26 raters, score from 1(bad) to 5(excellent) • The raters were calibrated on the NN(1) and HR(5) version of 20 images from BSD300 dataset
29Type A-3 Some Issues for Super Resolution • Metric Paper (The Perception-Distortion Tradeoff, 2018 CVPR) • Analysis distortion measure(PSNR, SSIM, etc.) vs human opinion score(Perceptual quality) • Good supplementary video: https://www.youtube.com/watch?v=6Yid4dituqo (PR-12) Reference: “The Perception-Distortion Tradeoff”, 2018 CVPR Better quality
30Type A-3 References • “Image Super-Resolution Using Deep Convolutional Networks”, 2014 ECCV • “Super Resolution Applications in Modern Digital Image Processing”, 2016 IJCA • “Loss Functions for Image Restoration with Neural Networks”, 2016 IEEE TCI • “Deconvolution and Checkerboard Artifacts”, distill blog(https://distill.pub/2016/deconv-checkerboard/) • “Accurate Image Super-Resolution Using Very Deep Convolutional Networks”, 2016 CVPR • “Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network”, 2016 CVPR • “Accelerating the Super-Resolution Convolutional Neural Network”, 2016 ECCV • “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network”, 2017 CVPR • http://www.vision.ee.ethz.ch/~timofter/publications/NTIRE2017SRchallenge_factsheets.pdf • “The Perception-Distortion Tradeoff”, 2018 CVPR • “Deep Learning for Single Image Super-Resolution: A Brief Review”, 2018 IEEE Transactions on Multimedia (TMM) • “A Deep Journey into Super-resolution: A survey”, 2019 arXiv
Thank you

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Single Image Super Resolution Overview

  • 1. Intelligence Machine Vision Lab Strictly Confidential Single Image Super Resolution Overview SUALAB Ho Seong Lee
  • 2. 2Type A-3 Contents • What is Super Resolution? • Applications of Super Resolution • Deep Learning for Single Image Super Resolution • Some Issues for Super Resolution
  • 3. 3Type A-3 What is Super Resolution? • Super Resolution • Restore High-Resolution(HR) image(or video) from Low-Resolution(LR) image(or video) • According to the number of input LR images, SR can be classified SISR or MISR • Single Image Super Resolution Efficient & Popular Super Resolution
  • 4. 4Type A-3 What is Super Resolution? • Single Image Super Resolution • Restore High-Resolution(HR) image(or video) from Low-Resolution(LR) image(or video) • Ill-Posed Problem.. (Regular Inverse Problem) → We can’t have ground truth from LR image HR 3 HR 2 HR 1 LR
  • 5. 5Type A-3 What is Super Resolution? • Interpolation-based Single Image Super Resolution • In image upscaling task, bicubic or bilinear or Lanczos interpolation is usually used. • Fast, easy.. but low quality.. Super Resolution Deep SR bilinear
  • 6. 6Type A-3 What is Super Resolution? • Single Image Super Resolution algorithms • Interpolation-based method • Reconstruction-based method • (Deep) Learning-based method • Today, I will cover learning-based method
  • 7. 7Type A-3 Applications of Super Resolution • Satellite image processing • Medical image processing • Multimedia Industry and Video Enhancement Reference: “Super Resolution Applications in Modern Digital Image Processing”, 2016 IJCA TV & Monitor HD(1280x720), FHD(1920x180) UHD(3840x2160)
  • 8. 8Type A-3 Deep Learning for Single Image Super Resolution • Learning-based Single Image Super Resolution • For tackling regular inverse problem, almost use this paradigm • [HR(GT) image] + [distortion & down-sampling] → [LR(input) image] • This is limitation of SISR training • Overall restoration quality is dependent on the distortion & down-sampling method Reference: “Deep Learning for Single Image Super-Resolution: A Brief Review”, 2018 IEEE Transactions on Multimedia (TMM)
  • 9. 9Type A-3 Deep Learning for Single Image Super Resolution • Learning-based Single Image Super Resolution • [HR(GT) image] + [distortion & down-sampling] → [LR(input) image] • In CVPR 2017 SR Challenge, many team showed many degradation of quality metric Reference: http://www.vision.ee.ethz.ch/~timofter/publications/NTIRE2017SRchallenge_factsheets.pdf
  • 10. 10Type A-3 Deep Learning for Single Image Super Resolution • First Deep Learning architecture for Single Image Super Resolution • SRCNN(2014) – three-layer CNN, MSE Loss, Early upsampling • Compared to traditional methods, it shows excellent performance. Reference: “Image Super-Resolution Using Deep Convolutional Networks”, 2014 ECCV
  • 11. 11Type A-3 Deep Learning for Single Image Super Resolution • Efficient Single Image Super Resolution • FSRCNN(2016), ESPCN(2016) • Use Late Upsampling with deconvolution or sub-pixel convolutional layer Inefficient in Memory, FLOPS Reference: “Image Super-Resolution Using Deep Convolutional Networks”, 2014 ECCV
  • 12. 12Type A-3 Deep Learning for Single Image Super Resolution • FSRCNN(Fast Super-Resolution Convolutional Neural Network) • Use Deconvolution layer instead of pre-processing(upsampling) • Faster and more accurate than SRCNN Reference: “Accelerating the Super-Resolution Convolutional Neural Network”, 2016 ECCV
  • 13. 13Type A-3 Deep Learning for Single Image Super Resolution • ESPCN(Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network) • Use sub-pixel convolutional layer (pixel shuffler or depth_to_space) • This sub-pixel convolutional layer is used in recent SR models Reference: “Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network”, 2016 CVPR Code: https://github.com/leftthomas/ESPCN
  • 14. 14Type A-3 Deep Learning for Single Image Super Resolution • Deeper Networks for Super-Resolution • SRCNN, FSRCNN, ESPCN are shallow network → Why not deep network? • Failed to train deeper models.. → Use shallow network → how to use deeper network? Reference: “Image Super-Resolution Using Deep Convolutional Networks”, 2014 ECCV
  • 15. 15Type A-3 Deep Learning for Single Image Super Resolution • VDSR(Accurate Image Super-Resolution Using Very Deep Convolutional Networks) • VGG based deeper model(20-layer) for Super-Resolution → large receptive field • Residual learning & High learning rate with gradient clipping • MSE Loss, Early upsampling Reference: “Accurate Image Super-Resolution Using Very Deep Convolutional Networks”, 2016 CVPR
  • 16. 16Type A-3 Deep Learning for Single Image Super Resolution • Deeper Networks for Super-Resolution after VDSR • DRCN(Deeply-recursive Convolutional network), 2016 CVPR • SRResNet, 2017 CVPR • DRRN(Deep Recursive Residual Network), 2017 CVPR Reference: “Deep Learning for Single Image Super-Resolution: A Brief Review”, 2018 IEEE Transactions on Multimedia (TMM)
  • 17. 17Type A-3 Deep Learning for Single Image Super Resolution • Deeper Networks for Super-Resolution after VDSR • EDSR, MDSR (Enhanced Deep Residual Network, Multi Scale EDSR), 2017 CVPRW • DenseSR, 2017 CVPR • MemNet, 2017 CVPR Reference: “Deep Learning for Single Image Super-Resolution: A Brief Review”, 2018 IEEE Transactions on Multimedia (TMM)
  • 18. 18Type A-3 Deep Learning for Single Image Super Resolution • Generative Adversarial Network(GAN) for Super-Resolution • SRGAN(Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network) • First GAN-based SR Model, MSE Loss → Blurry Output → GAN loss + Content loss = Perceptual loss Reference: “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network”, 2017 CVPR
  • 19. 19Type A-3 Deep Learning for Single Image Super Resolution • Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network • MSE Loss → Blurry Output → GAN loss + Content loss = Perceptual loss • Replace MSE loss to VGG loss (used in style transfer) and add adversarial loss Reference: “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network”, 2017 CVPR
  • 20. 20Type A-3 Deep Learning for Single Image Super Resolution • Generative Adversarial Network(GAN) for Super-Resolution • SRGAN, EnhanceNet, SRFeat, ESRGAN Reference: “A Deep Journey into Super-resolution: A survey”, 2019 arXiv
  • 21. 21Type A-3 Deep Learning for Single Image Super Resolution Reference: “A Deep Journey into Super-resolution: A survey”, 2019 arXiv • Deep Learning for Single Image Super Resolution
  • 22. 22Type A-3 Deep Learning for Single Image Super Resolution Reference: “A Deep Journey into Super-resolution: A survey”, 2019 arXiv
  • 23. 23Type A-3 Deep Learning for Single Image Super Resolution • Deep Learning for Single Image Super Resolution
  • 24. 24Type A-3 Some Issues for Super Resolution • Checkerboard artifact • Deconvolution (Transposed convolution) layer can easily have “uneven overlap” • Simple solution: use “resize + conv” or “sub-pixel convolutional layer” Reference: “Deconvolution and Checkerboard Artifacts”, distill blog(https://distill.pub/2016/deconv-checkerboard/)
  • 25. 25Type A-3 Some Issues for Super Resolution • Loss function • Propose a various loss function methods in Image Restoration task • Report the best result when using mixed loss with MS-SSIM loss + 𝒍 𝟏 loss Reference: “Loss Functions for Image Restoration with Neural Networks”, 2016 IEEE TCI
  • 26. 26Type A-3 Some Issues for Super Resolution • Loss function • Recent papers almost use 𝒍 𝟏 loss Reference: “A Deep Journey into Super-resolution: A survey”, 2019 arXiv
  • 27. 27Type A-3 Some Issues for Super Resolution • Metric (Distortion measure) • Almost paper use distortion metric(PSNR and SSIM) as performance metric • But, high PSNR, SSIM do not guarantee good quality that looks good to human.. Reference: “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network”, 2017 CVPR
  • 28. 28Type A-3 Some Issues for Super Resolution • Metric (Human opinion score) • But, high PSNR, SSIM do not guarantee good quality that looks good to human.. • In SRGAN Paper, Mean Opinion Score(MOS) Testing is done for quantify perceptual quality • 26 raters, score from 1(bad) to 5(excellent) • The raters were calibrated on the NN(1) and HR(5) version of 20 images from BSD300 dataset
  • 29. 29Type A-3 Some Issues for Super Resolution • Metric Paper (The Perception-Distortion Tradeoff, 2018 CVPR) • Analysis distortion measure(PSNR, SSIM, etc.) vs human opinion score(Perceptual quality) • Good supplementary video: https://www.youtube.com/watch?v=6Yid4dituqo (PR-12) Reference: “The Perception-Distortion Tradeoff”, 2018 CVPR Better quality
  • 30. 30Type A-3 References • “Image Super-Resolution Using Deep Convolutional Networks”, 2014 ECCV • “Super Resolution Applications in Modern Digital Image Processing”, 2016 IJCA • “Loss Functions for Image Restoration with Neural Networks”, 2016 IEEE TCI • “Deconvolution and Checkerboard Artifacts”, distill blog(https://distill.pub/2016/deconv-checkerboard/) • “Accurate Image Super-Resolution Using Very Deep Convolutional Networks”, 2016 CVPR • “Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network”, 2016 CVPR • “Accelerating the Super-Resolution Convolutional Neural Network”, 2016 ECCV • “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network”, 2017 CVPR • http://www.vision.ee.ethz.ch/~timofter/publications/NTIRE2017SRchallenge_factsheets.pdf • “The Perception-Distortion Tradeoff”, 2018 CVPR • “Deep Learning for Single Image Super-Resolution: A Brief Review”, 2018 IEEE Transactions on Multimedia (TMM) • “A Deep Journey into Super-resolution: A survey”, 2019 arXiv