From 87ce2b7345730ac73947e128d41441d8e489bdfc Mon Sep 17 00:00:00 2001 From: pathbreak <5163416+pathbreak@users.noreply.github.com> Date: Tue, 6 Apr 2021 22:47:38 +0530 Subject: [PATCH] dnn_superres: Fix arXiv URL typos in README.md --- modules/dnn_superres/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/dnn_superres/README.md b/modules/dnn_superres/README.md index b47772b049a..b8a4250ac83 100644 --- a/modules/dnn_superres/README.md +++ b/modules/dnn_superres/README.md @@ -40,7 +40,7 @@ Trained models can be downloaded from [here](https://github.com/fannymonori/TF-E - 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/1707.02921.pdf) [2] +- 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 @@ -66,7 +66,7 @@ Trained models can be downloaded from [here](https://github.com/fannymonori/TF-L - 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/1707.02921.pdf) [4] +- Original paper: [Deep laplacian pyramid networks for fast and accurate super-resolution](https://arxiv.org/pdf/1704.03915.pdf) [4] ### Benchmarks @@ -92,4 +92,4 @@ Refer to the benchmarks located in the tutorials for more detailed benchmarking. [3] Chao Dong, Chen Change Loy, Xiaoou Tang. **"Accelerating the Super-Resolution Convolutional Neural Network"**, in Proceedings of European Conference on Computer Vision **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"**, In Proceedings of the IEEE conference on computer vision and pattern recognition **CVPR 2017**. [[PDF](http://openaccess.thecvf.com/content_cvpr_2017/papers/Lai_Deep_Laplacian_Pyramid_CVPR_2017_paper.pdf)] [[arXiv](https://arxiv.org/abs/1710.01992)] [[Project Page](http://vllab.ucmerced.edu/wlai24/LapSRN/)] +[4] Lai, W. S., Huang, J. B., Ahuja, N., and Yang, M. H., **"Deep laplacian pyramid networks for fast and accurate super-resolution"**, In Proceedings of the IEEE conference on computer vision and pattern recognition **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/)]