Joint residual pyramid for joint image super-resolution

Yan Zheng1, Xiang Cao2, Yi Xiao2, Xianyi Zhu2, Jin Yuan2
1College of Electrical and Information Engineering, Hunan University, Changsha, Hunan Province, China
2College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan Province, China

Tài liệu tham khảo

Mac Aodha, 2015, Patch based synthesis for single depth image super-resolution, 71 Chen, 2017, Photographic image synthesis with cascaded refinement networks, 1520 Cheng, 2013, Efficient salient region detection with soft image abstraction, 1529 Diebel, 2005, An application of markov random fields to range sensing, NIPS, 291 Dong, 2014, Learning a deep convolutional network for image super-resolution, 184 Chao Dong, Chen Change Loy, Xiaoou Tang, Accelerating the super-resolution convolutional neural network, 2016. Available from: <1608.00367>. Ferstl, 2013, Image guided depth upsampling using anisotropic total generalized variation, 993 Ham, 2015, Robust image filtering using joint static and dynamic guidance, 4823 He, 2016, Deep residual learning for image recognition, vol. 00, 770 He, 2010, Guided image filtering, 1 He, 2016, Deep residual learning for image recognition, 770 Hirschmuller, 2007, Evaluation of cost functions for stereo matching, 1 Huang, 2017, Densely connected convolutional networks Hui, 2016, Depth map super-resolution by deep multi-scale guidance, 353 Jing, 2018, Low-rank multi-view embedding learning for micro-video popularity prediction, IEEE Trans. Knowl. Data Eng., 30, 1519, 10.1109/TKDE.2017.2785784 Kopf, 2007, Joint bilateral upsampling, 96 Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, 1097 Lei, 2017, Depth map super-resolution considering view synthesis quality, TIP, 26, 1732 Levin, 2004, Colorization using optimization, 689 Li, 2016, Deep joint image filtering, 154 Liu, 2013, Joint geodesic upsampling of depth images, 169 Lu, 2015, Sparse depth super resolution, 2245 Lu, 2014, Depth enhancement via low-rank matrix completion, 3390 Mathieu, 2016, Deep multi-scale video prediction beyond mean square error Nie, 2015, Disease inference from health-related questions via sparse deep learning, IEEE Trans. Knowl. Data Eng., 27, 2107, 10.1109/TKDE.2015.2399298 Nie, 2017, Modeling disease progression via multisource multitask learners: a case study with alzheimers disease, IEEE Trans. Neural Netw. Learn. Syst., 28, 1508, 10.1109/TNNLS.2016.2520964 Nie, 2017, Enhancing micro-video understanding by harnessing external sounds Park, 2011, High quality depth map upsampling for 3d-tof cameras, 1623 Ren, 2017, Image super resolution based on fusing multiple convolution neural networks, 1050 Russakovsky, 2015, ImageNet large scale visual recognition challenge, IJCV, 115, 211, 10.1007/s11263-015-0816-y Scharstein, 2002, A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, IJCV, 47, 7, 10.1023/A:1014573219977 Scharstein, 2007, Learning conditional random fields for stereo, 1 Shen, 2017, Convolutional neural pyramid for image processing, CoRR Silberman, 2012, Indoor segmentation and support inference from rgbd images, 746 Simonyan, 2014, Very deep convolutional networks for large-scale image recognition, Comput. Sci. Song, 2015, Sun rgb-d: a rgb-d scene understanding benchmark suite, 567 Xiao, 2018, Joint residual pyramid for depth map super-resolution, 797 Yang, 2014, Color-guided depth recovery from rgb-d data using an adaptive autoregressive model, TIP, 23, 3443 Yang, 2007, Spatial-depth super resolution for range images, 1