Saeed Anwar
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Abstract
Deep convolutional networks–based super-resolution is a fast-growing field with numerous practical applications. In this exposition, we extensively compare more than 30 state-of-the-art super-resolution Convolutional Neural Networks (CNNs) over three classical and three recently introduced challenging datasets to benchmark single image super-resolution. We introduce a taxonomy for deep learning–based super-resolution networks that groups existing methods into nine categories including linear, residual, multi-branch, recursive, progressive, attention-based, and adversarial designs. We also provide comparisons between the models in terms of network complexity, memory footprint, model input and output, learning details, the type of network losses, and important architectural differences (e.g., depth, skip-connections, filters). The extensive evaluation performed shows the consistent and rapid growth in the accuracy in the past few years along with a corresponding boost in model complexity and the availability of large-scale datasets. It is also observed that the pioneering methods identified as the benchmarks have been significantly outperformed by the current contenders. Despite the progress in recent years, we identify several shortcomings of existing techniques and provide future research directions towards the solution of these open problems. Datasets and codes for evaluation are publicly available at https://github.com/saeed-anwar/SRsurvey.
- Namhyuk Ahn, Byungkon Kang, and Kyung-Ah Sohn. 2018. Fast, accurate, and, lightweight super-resolution with cascading residual network. In Proceedings of the ECCV.Google Scholar
Cross Ref
- Saeed Anwar and Nick Barnes. 2019. Densely residual Laplacian super-resolution. arXiv (2019). Retreived 22 December 2019.Google Scholar
- Saeed Anwar, Cong Phouc Huynh, and Fatih Porikli. 2020. Identity enhanced image denoising. In IEEE Computer Vision and Pattern Recognition Workshops (CVPRW’20).Google Scholar
- Yancheng Bai, Yongqiang Zhang, Mingli Ding, and Bernard Ghanem. 2018. SOD-MTGAN: Small object detection via multi-task generative adversarial network. In Proceedings of the ECCV.Google ScholarCross Ref
- Marco Bevilacqua, Aline Roumy, Christine Guillemot, and Marie Line Alberi-Morel. 2012. Low-complexity single-image super-resolution based on nonnegative neighbor embedding. (2012).Google Scholar
- Yochai Blau, Roey Mechrez, Radu Timofte, Tomer Michaeli, and Lihi Zelnik-Manor. 2018. The 2018 PIRM challenge on perceptual image super-resolution. In Proceedings of the ECCV.Google Scholar
- Adrian Bulat, Jing Yang, and Georgios Tzimiropoulos. 2018. To learn image super-resolution, use a GAN to learn how to do image degradation first. In Proceedings of the ECCV.Google ScholarCross Ref
- Jianrui Cai, Shuhang Gu, Radu Timofte, and Lei Zhang. 2019. Ntire 2019 challenge on real image super-resolution: Methods and results. In Proceedings of the CVPRW.Google ScholarCross Ref
- Hong Chang, Dit-Yan Yeung, and Yimin Xiong. 2004. Super-resolution through neighbor embedding. In Proceedings of the CVPR.Google ScholarCross Ref
- J. Choi and M. Kim. 2017. A deep convolutional neural network with selection units for super-resolution. In Proceedings of the CVPRW.Google Scholar
- Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12 (2011), 2493--2537.Google ScholarCross Ref
- George E. Dahl, Dong Yu, Li Deng, and Alex Acero. 2012. Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. Trans. Audio, Speech Lang. Proc. 20, 1 (2012), 30--42.Google ScholarDigital Library
- Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In Conference on Computer Vision and Pattern Recognition. 248--255.Google ScholarCross Ref
- Chao Dong, Chen Loy, Kaiming He, and Xiaoou Tang. 2014. Learning a deep convolutional network for image super-resolution. In Proceedings of the ECCV.Google ScholarCross Ref
- Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. 2016. Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38, 2 (2016), 295--307.Google ScholarDigital Library
- Chao Dong, Chen Change Loy, and Xiaoou Tang. 2016. Accelerating the super-resolution convolutional neural network. In Proceedings of the ECCV.Google ScholarCross Ref
- Weisheng Dong, Zhangxi Yan, Xin Li, and Guangming Shi. 2018. Learning hybrid sparsity prior for image restoration: Where deep learning meets sparse coding. arXiv (2018). Accessed May 16, 2019.Google Scholar
- Y. Fan, H. Shi, J. Yu, D. Liu, W. Han, H. Yu, Z. Wang, X. Wang, and T. S. Huang. 2017. Balanced two-stage residual networks for image super-resolution. In Proceedings of the CVPRW.Google Scholar
- Raanan Fattal. 2007. Image upsampling via imposed edge statistics. ACM Trans. Graph. (2007), 95--es.Google Scholar
- William T. Freeman, Thouis R. Jones, and Egon C. Pasztor. 2002. Example-based super-resolution. IEEE Comput. Graph. Applic. 22, 2 (2002), 56--65.Google ScholarDigital Library
- Azuma Fujimoto, Toru Ogawa, Kazuyoshi Yamamoto, Yusuke Matsui, Toshihiko Yamasaki, and Kiyoharu Aizawa. 2016. Manga109 dataset and creation of metadata. In Proceedings of the MANPU @ ICPR.Google ScholarDigital Library
- Donald Geman and Chengda Yang. 1995. Nonlinear image recovery with half-quadratic regularization. IEEE Trans. Image Proc. 4, 7 (1995), 932--946.Google ScholarDigital Library
- Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. 2016. Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 38, 1 (2016), 142--158.Google ScholarDigital Library
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proceedings of the NIPS.Google ScholarDigital Library
- Hayit Greenspan. 2008. Super-resolution in medical imaging. Comput. J. 52, 1 (2008), 43--63.Google ScholarDigital Library
- Muhammad Haris, Greg Shakhnarovich, and Norimichi Ukita. 2018. Deep backprojection networks for super-resolution. In Proceedings of the CVPR.Google Scholar
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the ICCV.Google ScholarDigital Library
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the CVPR.Google ScholarCross Ref
- Jie Hu, Li Shen, and Gang Sun. 2018. Squeeze-and-excitation networks. In Proceedings of the CVPR.Google ScholarCross Ref
- Yanting Hu, Xinbo Gao, Jie Li, Yuanfei Huang, and Hanzi Wang. 2018. Single image super-resolution via cascaded multi-scale cross network. arXiv (2018). Accessed June 12, 2019.Google Scholar
- Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the CVPR.Google Scholar
- Jinggang Huang and David Mumford. 1999. Statistics of natural images and models. In Proceedings of the CVPR.Google Scholar
- Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja. 2015. Single image super-resolution from transformed self-exemplars. In Proceedings of the CVPR.Google ScholarCross Ref
- Zheng Hui, Xiumei Wang, and Xinbo Gao. 2018. Fast and accurate single image super-resolution via information distillation network. In Proceedings of the CVPR.Google ScholarCross Ref
- Michal Irani and Shmuel Peleg. 1991. Improving resolution by image registration. Comput. Vis. Image Underst. 53, 3 (1991), 231--239.Google Scholar
- Jianbo Jiao, Wei-Chih Tu, Shengfeng He, and Rynson W. H. Lau. 2017. FormResNet: Formatted residual learning for image restoration. In Proceedings of the CVPRW.Google Scholar
- Younghyun Jo, Seoung Wug Oh, Jaeyeon Kang, and Seon Joo Kim. 2018. Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In Proceedings of the CVPR. 3224--3232.Google ScholarCross Ref
- Justin Johnson, Alexandre Alahi, and Li Fei-Fei. 2016. Perceptual losses for real-time style transfer and super-resolution. In Proceedings of the ECCV.Google ScholarCross Ref
- Alexia Jolicoeur-Martineau. 2019. The relativistic discriminator: A key element missing from standard GAN. In Proceedings of the ICLR.Google Scholar
- Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, and Mohammed Bennamoun. 2018. A Guide to Convolutional Neural Networks for Computer Vision. Morgan 8 Claypool.Google Scholar
- Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. 2016. Accurate image super-resolution using very deep convolutional networks. In Proceedings of the CVPR.Google ScholarCross Ref
- Jun-Hyuk Kim, Jun-Ho Choi, Manri Cheon, and Jong-Seok Lee. 2018. RAM: Residual attention module for single image super-resolution. arXiv (2018). Accessed September 22, 2019.Google Scholar
- Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the ICLR.Google Scholar
- Ivan Krasin, Tom Duerig, Neil Alldrin, Vittorio Ferrari, Sami Abu-El-Haija, Alina Kuznetsova, Hassan Rom, Jasper Uijlings, Stefan Popov, Shahab Kamali, Matteo Malloci, Jordi Pont-Tuset, Andreas Veit, Serge Belongie, Victor Gomes, Abhinav Gupta, Chen Sun, Gal Chechik, David Cai, Zheyun Feng, Dhyanesh Narayanan, and Kevin Murphy. 2017. OpenImages: A public dataset for large-scale multi-label and multi-class image classification. Retrieved from: https://storage.googleapis.com/openimages/web/index.html.Google Scholar
- Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, and Richard Socher. 2016. Ask me anything: Dynamic memory networks for natural language processing. In Proceedings of the ICML.Google Scholar
- Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Tom Duerig, and Vittorio Ferrari. 2020. The open images dataset v4. International Journal of Computer Vision (2020), 1--26.Google ScholarCross Ref
- Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang. 2017. Deep Laplacian pyramid networks for fast and accurate superresolution. In Proceedings of the CVPR.Google Scholar
- Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang. 2018. Fast and accurate image super-resolution with deep Laplacian pyramid networks. IEEE Trans. Pattern Anal. Mach. Intell. 41, 11 (2018), 2599--2613.Google ScholarCross Ref
- Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew P. Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang et al. 2017. Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4681--4690.Google Scholar
- Chongyi Li, Saeed Anwar, and Fatih Porikli. 2020. Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recog. 98 (2020), 107038.Google ScholarDigital Library
- Sheng Li, Fengxiang He, Bo Du, Lefei Zhang, Yonghao Xu, and Dacheng Tao. 2019. Fast spatio-temporal residual network for video super-resolution. In Proceedings of the CVPR. 10522--10531.Google ScholarCross Ref
- Zhen Li, Jinglei Yang, Zheng Liu, Xiaomin Yang, Gwanggil Jeon, and Wei Wu. 2019. Feedback network for image super-resolution. In Proceedings of the CVPR. 3867--3876.Google ScholarCross Ref
- Thomas Lillesand, Ralph W. Kiefer, and Jonathan Chipman. 2014. Remote Sensing and Image Interpretation. John Wiley and Sons.Google Scholar
- Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. 2017. Enhanced deep residual networks for single image super-resolution. In Proceedings of the CVPRW.Google ScholarCross Ref
- Andrei P. Lobanov. 2005. Resolution limits in astronomical images. arXiv preprint astro-ph/0503225 (2005).Google Scholar
- Jonathan Long, Evan Shelhamer, and Trevor Darrell. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the CVPR.Google ScholarCross Ref
- Chao Ma, Chih-Yuan Yang, Xiaokang Yang, and Ming-Hsuan Yang. 2017. Learning a no-reference quality metric for single-image super-resolution. Comput. Vis. Image Underst. 158 (2017), 1--16.Google ScholarDigital Library
- Kede Ma, Zhengfang Duanmu, Qingbo Wu, Zhou Wang, Hongwei Yong, Hongliang Li, and Lei Zhang. 2017. Waterloo exploration database: New challenges for image quality assessment models. IEEE Trans. Image Proc. 26, 2 (2017), 1004--1016.Google ScholarDigital Library
- Andrew L. Maas, Awni Y. Hannun, and Andrew Y. Ng. 2013. Rectifier nonlinearities improve neural network acoustic models. In Proceedings of the ICML.Google Scholar
- Xiaojiao Mao, Chunhua Shen, and Yu-Bin Yang. 2016. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In Proceedings of the NIPS.Google Scholar
- David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik. 2001. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proceedings of the ICCV.Google ScholarCross Ref
- Anish Mittal, Anush Krishna Moorthy, and Alan Conrad Bovik. 2012. No-reference image quality assessment in the spatial domain. IEEE Trans. Image Proc. 21, 12 (2012), 4695--4708.Google ScholarDigital Library
- Anish Mittal, Rajiv Soundararajan, and Alan C. Bovik. 2012. Making a “completely blind” image quality analyzer. IEEE Sig. Proc. Lett. 20, 3 (2012), 209--212.Google ScholarCross Ref
- Sivaram Prasad Mudunuri and Soma Biswas. 2016. Low resolution face recognition across variations in pose and illumination. IEEE Trans. Pattern Anal. Mach. Intell. 38, 5 (2016), 1034--1040.Google ScholarDigital Library
- Seungjun Nah, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Radu Timofte, and Kyoung Mu Lee. 2019. NTIRE 2019 challenge on video deblurring and super-resolution: Dataset and study. In Proceedings of the CVPRW.Google Scholar
- Seong-Jin Park, Hyeongseok Son, Sunghyun Cho, Ki-Sang Hong, and Seungyong Lee. 2018. SRFeat: Single image super-resolution with feature discrimination. In Proceedings of the ECCV.Google ScholarCross Ref
- Ekta Prashnani, Hong Cai, Yasamin Mostofi, and Pradeep Sen. 2018. PieAPP: Perceptual image-error assessment through pairwise preference. In Proceedings of the CVPR.Google ScholarCross Ref
- Yajun Qiu, Ruxin Wang, Dapeng Tao, and Jun Cheng. 2019. Embedded block residual network: A recursive restoration model for single-image super-resolution. In Proceedings of the ICCV. 4180--4189.Google ScholarCross Ref
- Alec Radford, Luke Metz, and Soumith Chintala. 2016. Unsupervised representation learning with deep convolutional generative adversarial networks. In Proceedings of the ICLR.Google Scholar
- H. Ren, M. El-Khamy, and J. Lee. 2017. Image super resolution based on fusing multiple convolution neural networks. In Proceedings of the CVPRW.Google Scholar
- Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. In Proceedings of the NIPS.Google ScholarDigital Library
- Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of the MICCAI.Google ScholarCross Ref
- Mehdi S. M. Sajjadi, Bernhard Schölkopf, and Michael Hirsch. 2017. EnhanceNet: Single image super-resolution through automated texture synthesis. In Proceedings of the ICCV.Google ScholarCross Ref
- Hamid R. Sheikh, Alan C. Bovik, and Gustavo De Veciana. 2005. An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Proc. 14, 12 (2005), 2117--2128.Google ScholarDigital Library
- Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang. 2016. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In Proceedings of the CVPR.Google ScholarCross Ref
- Wenzhe Shi, Jose Caballero, Lucas Theis, Ferenc Huszar, Andrew Aitken, Christian Ledig, and Zehan Wang. 2016. Is the deconvolution layer the same as a convolutional layer?arXiv (2016). Accesses November 28, 2019.Google Scholar
- Assaf Shocher, Nadav Cohen, and Michal Irani. 2018. “Zero-shot” super-resolution using deep internal learning. CVPR (2018).Google Scholar
- Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In Proceedings of the ICLR.Google Scholar
- Edward Smith, Scott Fujimoto, and David Meger. 2018. Multi-view silhouette and depth decomposition for high resolution 3D object representation. In Proceedings of the NIPS. 6478--6488.Google Scholar
- Ashwin Swaminathan, Min Wu, and K. J. Ray Liu. 2008. Digital image forensics via intrinsic fingerprints. IEEE Trans. Inf. Forens. Secur. 3, 1 (2008), 101--117.Google ScholarDigital Library
- Ying Tai, Jian Yang, and Xiaoming Liu. 2017. Image super-resolution via deep recursive residual network. In Proceedings of the CVPR.Google ScholarCross Ref
- Ying Tai, Jian Yang, Xiaoming Liu, and Chunyan Xu. 2017. MemNet: A persistent memory network for image restoration. In Proceedings of the CVPR.Google ScholarCross Ref
- Omkar Thawakar, Prashant W. Patil, Akshay Dudhane, Subrahmanyam Murala, and Uday Kulkarni. 2019. Image and video super resolution using recurrent generative adversarial network. In Proceedings of the AVSS. IEEE, 1--8.Google ScholarCross Ref
- Radu Timofte, Eirikur Agustsson, Luc Van Gool, Ming-Hsuan Yang, Lei Zhang, Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, Kyoung Mu Lee, et al. 2017. Ntire 2017 challenge on single image super-resolution: Methods and results. In Proceedings of the CVPRW.Google ScholarCross Ref
- Radu Timofte, Shuhang Gu, Jiqing Wu, and Luc Van Gool. 2018. Ntire 2018 challenge on single image super-resolution: Methods and results. In Proceedings of the CVPRW.Google ScholarCross Ref
- Tong Tong, Gen Li, Xiejie Liu, and Qinquan Gao. 2017. Image super-resolution using dense skip connections. In Proceedings of the ICCV.Google ScholarCross Ref
- Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2018. Deep image prior. In Proceedings of the CVPR.Google Scholar
- Xintao Wang, Kelvin C. K. Chan, Ke Yu, Chao Dong, and Chen Change Loy. 2019. EDVR: Video restoration with enhanced deformable convolutional networks. In Proceedings of the CVPR. 0--0.Google ScholarCross Ref
- Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Yu Qiao, and Xiaoou Tang. 2018. ESRGAN: Enhanced super-resolution generative adversarial networks. In Proceedings of the ECCVW.Google Scholar
- Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. 2004. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Proc. 13, 4 (2004), 600--612.Google ScholarDigital Library
- Zhaowen Wang, Ding Liu, Jianchao Yang, Wei Han, and Thomas Huang. 2015. Deep networks for image super-resolution with sparse prior. In Proceedings of the ICCV.Google ScholarCross Ref
- Z. Wang, E. P. Simoncelli, and A. C. Bovik. 2003. Multiscale structural similarity for image quality assessment. In Proceedings of the ACSSC.Google Scholar
- Chih-Yuan Yang, Chao Ma, and Ming-Hsuan Yang. 2014. Single-image super-resolution: A benchmark. In Proceedings of the ECCV.Google ScholarCross Ref
- Jianchao Yang, Zhe Lin, and Scott Cohen. 2013. Fast image super-resolution based on in-place example regression. In Proceedings of the CVPR.Google ScholarDigital Library
- Jianchao Yang, John Wright, Thomas S. Huang, and Yi Ma. 2010. Image super-resolution via sparse representation. IEEE Trans. Image Proc. 19, 11 (2010), 2861--2873.Google ScholarDigital Library
- Yuan Yuan, Siyuan Liu, Jiawei Zhang, Yongbing Zhang, Chao Dong, and Liang Lin. 2018. Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks. In Proceedings of the CVPRW.Google ScholarCross Ref
- Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. 2019. Learning enriched features for real image restoration and enhancement. arXiv preprint 2003.06792 (2019).Google Scholar
- Roman Zeyde, Michael Elad, and Matan Protter. 2010. On single image scale-up using sparse-representations. In Proceedings of the ICCS.Google Scholar
- Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, and Lei Zhang. 2017. Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Trans. Image Proc. 26, 7 (2017), 3142--3155.Google ScholarDigital Library
- Kai Zhang, Wangmeng Zuo, Shuhang Gu, and Lei Zhang. 2017. Learning deep CNN denoiser prior for image restoration. In Proceedings of the CVPR.Google ScholarCross Ref
- Kai Zhang, Wangmeng Zuo, and Lei Zhang. 2018. Learning a single convolutional super-resolution network for multiple degradations. In Proceedings of the CVPR.Google ScholarCross Ref
- Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. 2018. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the CVPR.Google ScholarCross Ref
- Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. 2018. Image super-resolution using very deep residual channel attention networks. In Proceedings of the ECCV.Google ScholarCross Ref
- Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu. 2018. Residual dense network for image super-resolution. In Proceedings of the CVPR.Google ScholarCross Ref
Index Terms
- A Deep Journey into Super-resolution: A Survey
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