Cascaded Random Forests for Fast Image Super-Resolution
暂无分享,去创建一个
[1] Srimanta Mandal,et al. Edge preserving single image super resolution in sparse environment , 2013, 2013 IEEE International Conference on Image Processing.
[2] Michal Irani,et al. Super-resolution from a single image , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[3] Luc Van Gool,et al. A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution , 2014, ACCV.
[4] Sunghyun Cho,et al. Good Image Priors for Non-blind Deconvolution - Generic vs. Specific , 2014, ECCV.
[5] Wan-Chi Siu,et al. Image super-resolution via weighted random forest , 2017, 2017 IEEE International Conference on Industrial Technology (ICIT).
[6] Jian Yang,et al. MemNet: A Persistent Memory Network for Image Restoration , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[7] Kyoung Mu Lee,et al. Deeply-Recursive Convolutional Network for Image Super-Resolution , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Jian Yang,et al. Image Super-Resolution via Deep Recursive Residual Network , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Yair Weiss,et al. From learning models of natural image patches to whole image restoration , 2011, 2011 International Conference on Computer Vision.
[10] Raanan Fattal,et al. Image and video upscaling from local self-examples , 2011, TOGS.
[11] Wan-Chi Siu,et al. Fast Image Interpolation via Random Forests , 2015, IEEE Transactions on Image Processing.
[12] Xuelong Li,et al. Single image super resolution with high resolution dictionary , 2011, 2011 18th IEEE International Conference on Image Processing.
[13] Wan-Chi Siu,et al. Single image super-resolution using Gaussian process regression , 2011, CVPR 2011.
[14] Jitendra Malik,et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.
[15] Luc Van Gool,et al. Anchored Neighborhood Regression for Fast Example-Based Super-Resolution , 2013, 2013 IEEE International Conference on Computer Vision.
[16] Narendra Ahuja,et al. Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Guangming Shi,et al. Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach , 2013, IEEE Transactions on Image Processing.
[18] Yui-Lam Chan,et al. Fast image super-resolution via Randomized Multi-split Forests , 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).
[19] Thomas S. Huang,et al. Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.
[20] Xiaoou Tang,et al. Accelerating the Super-Resolution Convolutional Neural Network , 2016, ECCV.
[21] Kyoung Mu Lee,et al. Enhanced Deep Residual Networks for Single Image Super-Resolution , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[22] Xiaoou Tang,et al. Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] Aggelos K. Katsaggelos,et al. Super-resolution of compressed videos using convolutional neural networks , 2016, 2016 IEEE International Conference on Image Processing (ICIP).
[24] Wan-Chi Siu,et al. Image super-resolution via hybrid NEDI and wavelet-based scheme , 2015, 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA).
[25] Wan-Chi Siu,et al. Learning Hierarchical Decision Trees for Single-Image Super-Resolution , 2017, IEEE Transactions on Circuits and Systems for Video Technology.