![]() ![]() Wang, Zhaowen and Liu, Ding and Yang, Jianchao and Han, Wei and Huang, Thomas, Deep networks for image super-resolution with sparse prior, ICCV, 2015. (use more training data and achieve better SR performance.) Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Image Super-Resolution Using Deep Convolutional Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016. (first introduce CNN to solve single image super-resolution.) Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a deep convolutional network for image super-resolution, ECCV, 2014. Timofte, Radu and Rothe, Rasmus and Van Gool, Luc, Seven Ways to Improve Example-Based Single Image Super Resolution, CVPR, 2016. Rosenhahn, PS圜o: Manifold Span Reduction for Super Resolution, CVPR, 2016. Salvador, Jordi, and Eduardo Pérez-Pellitero, Naive Bayes Super-Resolution Forest, ICCV, 2015. Schulter, Samuel and Leistner, Christian and Bischof, Horst, Fast and accurate image upscaling with super-resolution forests, CVPR, 2015. Timofte, Radu and De Smet, Vincent and Van Gool, Luc, A+: Adjusted anchored neighborhood regression for fast super-resolution, ACCV, 2014. Yang, Chih-Yuan and Yang, Ming-Hsuan, Fast direct super-resolution by simple functions, ICCV, 2013. Timofte, Radu and De Smet, Vincent and Van Gool, Luc, Anchored neighborhood regression for fast example-based super-resolution, ICCV, 2013. Gu, Shuhang and Sang, Nong and Ma, Fan, Fast Image Super Resolution via Local Regression, ICPR, 2012. Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja, "Single Image Super-Resolution from Transformed Self-Exemplars", CVPR, 2015. Daniel Glasner, Shai Bagon and Michal, Irani, Super-Resolution from a Single Image, ICCV, 2009. ![]() (Predict the relationships between Low-resolution and high-resolution representation coefficients.) Super-resolution via self-examplars Peleg, Tomer and Elad, Michael, A statistical prediction model based on sparse representations for single image super-resolution, TIP, 2014. Lei Zhang's and Weisheng Dong's Website!) (Clustering is a very effective trick and local and nonlocal regularization terms are very powerful! Other good sparsity-based super-resolution methods can be found in Prof. Weisheng Dong, Lei Zhang, Guangming Shi, and Xiaolin Wu, Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization, TIP, 2011. ![]() Many sparsity-based image restoration techniques can be found in Prof. (Low dimension feature speeds up the algorithm. Zeyde, Roman and Elad, Michael and Protter, Matan, On single image scale-up using sparse-representations, International conference on curves and surfaces, 2010. (SCSR: Classical sparsity-based SISR method - use sparse coding technique to learn low-resolution and high-resolution dictionaries.) Yang, Jianchao and Wright, John and Huang, Thomas S and Ma, Yi, Image super-resolution via sparse representation, IEEE trans. (The idea that low-resolution patches and corresponding high-resolution patches share similar local geometries highly influences the subsequent coding-based or dictionary-based methods.) Sparsity-based methods Chang, Hong and Yeung, Dit-Yan and Xiong, Yimin, Super-resolution through neighbor embedding, CVPR, 2004. Freeman, William T and Jones, Thouis R and Pasztor, Egon C, Example-based super-resolution, IEEE Computer graphics and Applications, 2002. first presented example-based or learning-based super-resolution framework - learn relationships between low-resolution image patches and its high-resolution counterparts.) Freeman, William T and Pasztor, Egon C and Carmichael, Owen T, Learning low-level vision, IJCV, 2000. Example-based methods Early learning-based methods Deep Learning for Single Image Super-Resolution:Ī Brief Review. Wenming Yang, Xuechen Zhang, Yapeng Tian, Wei Wang, Jing-Hao Xue. īy Yapeng Tian, Yunlun Zhang, Xiaoyu Xiang (if you have any suggestions, please contact us! Email: OR OR For SR beginners, I recommend you to read some early learning based SISR works which will help understand the problem. The timestamp is only as accurate as the clock in the camera, and it may be completely wrong.A list of resources for example-based single image super-resolution, inspired by Awesome-deep-vision and Awesome Computer Vision. If the file has been modified from its original state, some details such as the timestamp may not fully reflect those of the original file. This file contains additional information such as Exif metadata which may have been added by the digital camera, scanner, or software program used to create or digitize it. ![]()
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