Build receptive pyramid for efficient color image compression artifact reduction
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Rongxin Jiang | Bolun Zheng | Xiang Tian | Yaowu Chen | Xuesong Liu | Rongxin Jiang | Xiang Tian | Bolun Zheng | Xuesong Liu | Yao-wu Chen
[1] David G. Lowe,et al. Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[2] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Yu-Bin Yang,et al. Learning Deep Representations Using Convolutional Auto-Encoders with Symmetric Skip Connections , 2016, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[4] Alan C. Bovik,et al. A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms , 2006, IEEE Transactions on Image Processing.
[5] Yun Fu,et al. Residual Dense Network for Image Super-Resolution , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[6] G LoweDavid,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004 .
[7] Alessandro Foi,et al. Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.
[8] Eero P. Simoncelli,et al. Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.
[9] Vladlen Koltun,et al. Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.
[10] Karen O. Egiazarian,et al. Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images , 2007, IEEE Transactions on Image Processing.
[11] Lei Zhang,et al. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.
[12] Wuzhen Shi,et al. Single image super-resolution with dilated convolution based multi-scale information learning inception module , 2017, 2017 IEEE International Conference on Image Processing (ICIP).
[13] Rui Sun,et al. S-Net: a scalable convolutional neural network for JPEG compression artifact reduction , 2018, J. Electronic Imaging.
[14] Luc Van Gool,et al. NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[15] Xiaoou Tang,et al. Compression Artifacts Reduction by a Deep Convolutional Network , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[16] Luca Benini,et al. CAS-CNN: A deep convolutional neural network for image compression artifact suppression , 2016, 2017 International Joint Conference on Neural Networks (IJCNN).
[17] Cheolkon Jung,et al. DCSR: Dilated Convolutions for Single Image Super-Resolution , 2019, IEEE Transactions on Image Processing.
[18] Charless C. Fowlkes,et al. Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[19] Hongyang Chao,et al. Building Dual-Domain Representations for Compression Artifacts Reduction , 2016, ECCV.
[20] Wenhan Yang,et al. Dmcnn: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal , 2018, 2018 25th IEEE International Conference on Image Processing (ICIP).
[21] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[22] Jian Yang,et al. MemNet: A Persistent Memory Network for Image Restoration , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[23] Graham W. Taylor,et al. Adaptive deconvolutional networks for mid and high level feature learning , 2011, 2011 International Conference on Computer Vision.