Wavelet Domain Generative Adversarial Network for Multi-scale Face Hallucination
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Tieniu Tan | Zhenan Sun | Ran He | Huaibo Huang | T. Tan | Zhenan Sun | R. He | Huaibo Huang
[1] Xin Yu,et al. Hallucinating Very Low-Resolution Unaligned and Noisy Face Images by Transformative Discriminative Autoencoders , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Peyman Milanfar,et al. INTERPOLATION-RESTORATION METHOD FOR SUPERRESOLUTION (WAVELET SUPERRESOLUTION)* , 2000 .
[3] Harry Shum,et al. Face Hallucination: Theory and Practice , 2007, International Journal of Computer Vision.
[4] Xin Yu,et al. Face Hallucination with Tiny Unaligned Images by Transformative Discriminative Neural Networks , 2017, AAAI.
[5] Xin Yu,et al. Ultra-Resolving Face Images by Discriminative Generative Networks , 2016, ECCV.
[6] Mohammed Bennamoun,et al. Empowering Simple Binary Classifiers for Image Set Based Face Recognition , 2017, International Journal of Computer Vision.
[7] Hua Han,et al. Wavelet-domain HMT-based image super-resolution , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).
[8] Cornelia Fermüller,et al. Robust Wavelet-Based Super-Resolution Reconstruction: Theory and Algorithm , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[9] Stéphane Mallat,et al. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..
[10] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[11] Xiaogang Wang,et al. Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[12] H. Demirel,et al. Image Super Resolution Based on Interpolation of Wavelet Domain High Frequency Subbands and the Spatial Domain Input Image , 2010 .
[13] Chi-Keung Tang,et al. Limits of Learning-Based Superresolution Algorithms , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[14] Ruimin Hu,et al. Noise Robust Face Hallucination via Locality-Constrained Representation , 2014, IEEE Transactions on Multimedia.
[15] Xiaoou Tang,et al. Deep Cascaded Bi-Network for Face Hallucination , 2016, ECCV.
[16] Georgios Tzimiropoulos,et al. Super-FAN: Integrated Facial Landmark Localization and Super-Resolution of Real-World Low Resolution Faces in Arbitrary Poses with GANs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[17] Tieniu Tan,et al. IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis , 2018, NeurIPS.
[18] Jing Yang,et al. To learn image super-resolution, use a GAN to learn how to do image degradation first , 2018, ECCV.
[19] Stéphane Mallat,et al. Wavelets for a vision , 1996, Proc. IEEE.
[20] Stéphane Mallat,et al. Understanding deep convolutional networks , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[21] Bernhard Schölkopf,et al. EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[22] Raymond Y. K. Lau,et al. Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[23] Chih-Yuan Yang,et al. Structured Face Hallucination , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[24] Shiguang Shan,et al. Aligning Coupled Manifolds for Face Hallucination , 2009, IEEE Signal Processing Letters.
[25] Thomas S. Huang,et al. Face hallucination VIA sparse coding , 2008, 2008 15th IEEE International Conference on Image Processing.
[26] Xiaogang Wang,et al. Hallucinating face by eigentransformation , 2005, IEEE Trans. Syst. Man Cybern. Part C.
[27] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Chun Qi,et al. Hallucinating face by position-patch , 2010, Pattern Recognit..
[29] Jian Yang,et al. FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[30] Jonathon Shlens,et al. Conditional Image Synthesis with Auxiliary Classifier GANs , 2016, ICML.
[31] Ming-Hsuan Yang,et al. Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[32] Daniel Rueckert,et al. Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[33] Narendra Ahuja,et al. Super-resolving Noisy Images , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[34] Mohammad Norouzi,et al. Pixel Recursive Super Resolution , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[35] Chih-Yuan Yang,et al. Fast Direct Super-Resolution by Simple Functions , 2013, 2013 IEEE International Conference on Computer Vision.
[36] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[37] Sapan Naik,et al. Single image super resolution in spatial and wavelet domain , 2013, ArXiv.
[38] Kyoung Mu Lee,et al. Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Xing Gao,et al. A hybrid wavelet convolution network with sparse-coding for image super-resolution , 2016, 2016 IEEE International Conference on Image Processing (ICIP).
[40] Chih-Yuan Yang,et al. Hallucinating Compressed Face Images , 2017, International Journal of Computer Vision.
[41] Lucas Theis,et al. Amortised MAP Inference for Image Super-resolution , 2016, ICLR.
[42] Deqing Sun,et al. Learning to Super-Resolve Blurry Face and Text Images , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[43] Lior Shamir,et al. Evaluation of Face Datasets as Tools for Assessing the Performance of Face Recognition Methods , 2008, International Journal of Computer Vision.
[44] Reuben A. Farrugia,et al. Face Hallucination Using Linear Models of Coupled Sparse Support , 2015, IEEE Transactions on Image Processing.
[45] Tong Tong,et al. Image Super-Resolution Using Dense Skip Connections , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[46] Thomas S. Huang,et al. Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.
[47] Xin Yu,et al. Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[48] Tieniu Tan,et al. Coupled Deep Learning for Heterogeneous Face Recognition , 2017, AAAI.
[49] Kyoung Mu Lee,et al. Deeply-Recursive Convolutional Network for Image Super-Resolution , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[51] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[52] Tieniu Tan,et al. Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[53] Marwan Mattar,et al. Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .
[54] Jaakko Lehtinen,et al. Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.
[55] Dimitris N. Metaxas,et al. StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[56] Li Fei-Fei,et al. Perceptual Losses for Real-Time Style Transfer and Super-Resolution , 2016, ECCV.
[57] Ronald R. Coifman,et al. Entropy-based algorithms for best basis selection , 1992, IEEE Trans. Inf. Theory.
[58] Xin Yu,et al. Face Super-Resolution Guided by Facial Component Heatmaps , 2018, ECCV.
[59] Joan Bruna,et al. Super-Resolution with Deep Convolutional Sufficient Statistics , 2015, ICLR.
[60] 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).
[61] Alex Graves,et al. Conditional Image Generation with PixelCNN Decoders , 2016, NIPS.
[62] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[63] Leon A. Gatys,et al. Image Style Transfer Using Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[64] D. Yeung,et al. Super-resolution through neighbor embedding , 2004, CVPR 2004.
[65] Xuelong Li,et al. A Comprehensive Survey to Face Hallucination , 2013, International Journal of Computer Vision.
[66] Xiaoou Tang,et al. Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[67] Maoguo Gong,et al. Position-Patch Based Face Hallucination Using Convex Optimization , 2011, IEEE Signal Processing Letters.
[68] Andrew Zisserman,et al. Deep Face Recognition , 2015, BMVC.
[69] Jian Yang,et al. Image Super-Resolution via Deep Recursive Residual Network , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[70] Seong-Whan Lee,et al. An Example-Based Face Hallucination Method for Single-Frame, Low-Resolution Facial Images , 2008, IEEE Transactions on Image Processing.
[71] Narendra Ahuja,et al. Single image super-resolution from transformed self-exemplars , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).