vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

modules/dnn_superres/README.md

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+# Super Resolution using Convolutional Neural Networks
+
+This module contains several learning-based algorithms for upscaling an image.
+
+## Usage
+
+Run the following command to build this module:
+
+```make
+cmake -DOPENCV_EXTRA_MODULES_PATH=<opencv_contrib>/modules -Dopencv_dnn_superres=ON <opencv_source_dir>
+```
+
+Refer to the tutorials to understand how to use this module.
+
+## Models
+
+There are four models which are trained.
+
+#### EDSR
+
+Trained models can be downloaded from [here](https://github.com/Saafke/EDSR_Tensorflow/tree/master/models).
+
+- Size of the model: ~38.5MB. This is a quantized version, so that it can be uploaded to GitHub. (Original was 150MB.)
+- This model was trained for 3 days with a batch size of 16
+- Link to implementation code: https://github.com/Saafke/EDSR_Tensorflow
+- x2, x3, x4 trained models available
+- Advantage: Highly accurate
+- Disadvantage: Slow and large filesize
+- Speed: < 3 sec for every scaling factor on 256x256 images on an Intel i7-9700K CPU.
+- Original paper: [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://arxiv.org/pdf/1707.02921.pdf) [1]
+
+#### ESPCN
+
+Trained models can be downloaded from [here](https://github.com/fannymonori/TF-ESPCN/tree/master/export).
+
+- Size of the model: ~100kb
+- This model was trained for ~100 iterations with a batch size of 32
+- Link to implementation code: https://github.com/fannymonori/TF-ESPCN
+- x2, x3, x4 trained models available
+- Advantage: It is tiny and fast, and still performs well.
+- Disadvantage: Perform worse visually than newer, more robust models.
+- Speed: < 0.01 sec for every scaling factor on 256x256 images on an Intel i7-9700K CPU.
+- Original paper: [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://arxiv.org/pdf/1609.05158.pdf) [2]
+
+#### FSRCNN
+
+Trained models can be downloaded from [here](https://github.com/Saafke/FSRCNN_Tensorflow/tree/master/models).
+
+- Size of the model: ~40KB (~9kb for FSRCNN-small)
+- This model was trained for ~30 iterations with a batch size of 1
+- Link to implementation code: https://github.com/Saafke/FSRCNN_Tensorflow
+- Advantage: Fast, small and accurate
+- Disadvantage: Not state-of-the-art accuracy
+- Speed: < 0.01 sec for every scaling factor on 256x256 images on an Intel i7-9700K CPU.
+- Notes: FSRCNN-small has fewer parameters, thus less accurate but faster.
+- Original paper: [Accelerating the Super-Resolution Convolutional Neural Network](http://mmlab.ie.cuhk.edu.hk/projects/FSRCNN.html) [3]
+
+#### LapSRN
+
+Trained models can be downloaded from [here](https://github.com/fannymonori/TF-LapSRN/tree/master/export).
+
+- Size of the model: between 1-5Mb
+- This model was trained for ~50 iterations with a batch size of 32
+- Link to implementation code: https://github.com/fannymonori/TF-LAPSRN
+- x2, x4, x8 trained models available
+- Advantage: The model can do multi-scale super-resolution with one forward pass. It can now support 2x, 4x, 8x, and [2x, 4x] and [2x, 4x, 8x] super-resolution.
+- Disadvantage: It is slower than ESPCN and FSRCNN, and the accuracy is worse than EDSR.
+- Speed: < 0.1 sec for every scaling factor on 256x256 images on an Intel i7-9700K CPU.
+- Original paper: [Deep laplacian pyramid networks for fast and accurate super-resolution](https://arxiv.org/pdf/1704.03915.pdf) [4]
+
+### Benchmarks
+
+Comparing different algorithms. Scale x4 on monarch.png (768x512 image).
+
+|               | Inference time in seconds (CPU)| PSNR | SSIM |
+| ------------- |:-------------------:| ---------:|--------:|
+| ESPCN            |0.01159   | 26.5471 | 0.88116 |
+| EDSR             |3.26758     |**29.2404**  |**0.92112**  |
+| FSRCNN           | 0.01298   | 26.5646 | 0.88064 |
+| LapSRN           |0.28257    |26.7330   |0.88622  |
+| Bicubic          |0.00031 |26.0635  |0.87537  |
+| Nearest neighbor |**0.00014** |23.5628  |0.81741  |
+| Lanczos          |0.00101  |25.9115  |0.87057  |
+
+Refer to the benchmarks located in the tutorials for more detailed benchmarking.
+
+### References
+[1] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee, **"Enhanced Deep Residual Networks for Single Image Super-Resolution"**, <i> 2nd NTIRE: New Trends in Image Restoration and Enhancement workshop and challenge on image super-resolution in conjunction with **CVPR 2017**. </i> [[PDF](http://openaccess.thecvf.com/content_cvpr_2017_workshops/w12/papers/Lim_Enhanced_Deep_Residual_CVPR_2017_paper.pdf)] [[arXiv](https://arxiv.org/abs/1707.02921)] [[Slide](https://cv.snu.ac.kr/research/EDSR/Presentation_v3(release).pptx)]
+
+[2] Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A., Bishop, R., Rueckert, D. and Wang, Z., **"Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network"**, <i>Proceedings of the IEEE conference on computer vision and pattern recognition</i> **CVPR 2016**. [[PDF](http://openaccess.thecvf.com/content_cvpr_2016/papers/Shi_Real-Time_Single_Image_CVPR_2016_paper.pdf)] [[arXiv](https://arxiv.org/abs/1609.05158)]
+
+[3] Chao Dong, Chen Change Loy, Xiaoou Tang. **"Accelerating the Super-Resolution Convolutional Neural Network"**, <i> in Proceedings of European Conference on Computer Vision </i>**ECCV 2016**. [[PDF](http://personal.ie.cuhk.edu.hk/~ccloy/files/eccv_2016_accelerating.pdf)]
+[[arXiv](https://arxiv.org/abs/1608.00367)] [[Project Page](http://mmlab.ie.cuhk.edu.hk/projects/FSRCNN.html)]
+
+[4] Lai, W. S., Huang, J. B., Ahuja, N., and Yang, M. H., **"Deep laplacian pyramid networks for fast and accurate super-resolution"**, <i> In Proceedings of the IEEE conference on computer vision and pattern recognition </i>**CVPR 2017**. [[PDF](http://openaccess.thecvf.com/content_cvpr_2017/papers/Lai_Deep_Laplacian_Pyramid_CVPR_2017_paper.pdf)] [[arXiv](https://arxiv.org/abs/1704.03915)] [[Project Page](http://vllab.ucmerced.edu/wlai24/LapSRN/)]