LBCRN: lightweight bidirectional correction residual network for image super-resolution

[1]  Jie Zhou,et al.  Efficient Non-Local Contrastive Attention for Image Super-Resolution , 2022, AAAI.

[2]  Lei Zhang,et al.  Edge-oriented Convolution Block for Real-time Super Resolution on Mobile Devices , 2021, ACM Multimedia.

[3]  Yuchen Fan,et al.  Image Super-Resolution with Non-Local Sparse Attention , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[4]  Ningning Ma,et al.  RepVGG: Making VGG-style ConvNets Great Again , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Nick Barnes,et al.  A Deep Journey into Super-resolution , 2019, ACM Comput. Surv..

[6]  Yun Fu,et al.  Residual Dense Network for Image Restoration , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Yongwoo Kim,et al.  Multi-attention Based Ultra Lightweight Image Super-Resolution , 2020, ECCV Workshops.

[8]  W. Zuo,et al.  Lightweight image super-resolution with enhanced CNN , 2020, Knowl. Based Syst..

[9]  Jie Tang,et al.  Residual Feature Aggregation Network for Image Super-Resolution , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Mingkui Tan,et al.  Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  W. Zuo,et al.  ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Jun-Ho Choi,et al.  MAMNet: Multi-path adaptive modulation network for image super-resolution , 2018, Neurocomputing.

[13]  Jie Li,et al.  Channel-Wise and Spatial Feature Modulation Network for Single Image Super-Resolution , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  Enhua Wu,et al.  Squeeze-and-Excitation Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Weili Zeng,et al.  Fast Single-Image Super-Resolution via Deep Network With Component Learning , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[16]  Peihua Li,et al.  Resolution-Aware Network for Image Super-Resolution , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[17]  Nick Barnes,et al.  A Deep Journey into Super-resolution , 2019, ACM Computing Surveys.

[18]  Wei Wu,et al.  Feedback Network for Image Super-Resolution , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Yun Fu,et al.  Residual Non-local Attention Networks for Image Restoration , 2019, ICLR.

[20]  In-So Kweon,et al.  CBAM: Convolutional Block Attention Module , 2018, ECCV.

[21]  Yun Fu,et al.  Image Super-Resolution Using Very Deep Residual Channel Attention Networks , 2018, ECCV.

[22]  Nenghai Yu,et al.  Model-Level Dual Learning , 2018, ICML.

[23]  Chao Dong,et al.  Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[24]  Xinbo Gao,et al.  Fast and Accurate Single Image Super-Resolution via Information Distillation Network , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[25]  Kyung-Ah Sohn,et al.  Fast, Accurate, and, Lightweight Super-Resolution with Cascading Residual Network , 2018, ECCV.

[26]  Jun Sun,et al.  Robust Single-Image Super-Resolution Based on Adaptive Edge-Preserving Smoothing Regularization , 2018, IEEE Transactions on Image Processing.

[27]  Yun Fu,et al.  Residual Dense Network for Image Super-Resolution , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[28]  Wangmeng Zuo,et al.  Learning a Single Convolutional Super-Resolution Network for Multiple Degradations , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[29]  Huchuan Lu,et al.  Deep Mutual Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[30]  Jian Yang,et al.  MemNet: A Persistent Memory Network for Image Restoration , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[31]  Nenghai Yu,et al.  Dual Inference for Machine Learning , 2017, IJCAI.

[32]  Eirikur Agustsson,et al.  NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[33]  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).

[34]  Nenghai Yu,et al.  Dual Supervised Learning , 2017, ICML.

[35]  Jian Yang,et al.  Image Super-Resolution via Deep Recursive Residual Network , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Wan-Chi Siu,et al.  Learning Hierarchical Decision Trees for Single-Image Super-Resolution , 2017, IEEE Transactions on Circuits and Systems for Video Technology.

[37]  Xiaogang Wang,et al.  Residual Attention Network for Image Classification , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  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).

[39]  Ping Tan,et al.  DualGAN: Unsupervised Dual Learning for Image-to-Image Translation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[40]  Tat-Seng Chua,et al.  SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[41]  Xiaoou Tang,et al.  Accelerating the Super-Resolution Convolutional Neural Network , 2016, ECCV.

[42]  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).

[43]  Kyoung Mu Lee,et al.  Deeply-Recursive Convolutional Network for Image Super-Resolution , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[44]  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).

[45]  Ruslan Salakhutdinov,et al.  Generating Images from Captions with Attention , 2015, ICLR.

[46]  Kiyoharu Aizawa,et al.  Sketch-based manga retrieval using manga109 dataset , 2015, Multimedia Tools and Applications.

[47]  Narendra Ahuja,et al.  Single image super-resolution from transformed self-exemplars , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[48]  Yoshua Bengio,et al.  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.

[49]  Xiaoou Tang,et al.  Learning a Deep Convolutional Network for Image Super-Resolution , 2014, ECCV.

[50]  Aline Roumy,et al.  Low-Complexity Single-Image Super-Resolution based on Nonnegative Neighbor Embedding , 2012, BMVC.

[51]  Xuelong Li,et al.  Partially Supervised Neighbor Embedding for Example-Based Image Super-Resolution , 2011, IEEE Journal of Selected Topics in Signal Processing.

[52]  Michael Elad,et al.  On Single Image Scale-Up Using Sparse-Representations , 2010, Curves and Surfaces.

[53]  Michael Elad,et al.  Generalizing the Nonlocal-Means to Super-Resolution Reconstruction , 2009, IEEE Transactions on Image Processing.

[54]  H. Shum,et al.  Image super-resolution using gradient profile prior , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[55]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[56]  M. Orchard,et al.  New edge-directed interpolation , 2001, IEEE Trans. Image Process..

[57]  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.

[58]  Youji Iiguni,et al.  Image interpolation for progressive transmission by using radial basis function networks , 1999, IEEE Trans. Neural Networks.