Cascade Convolutional Neural Network for Image Super-Resolution

With the development of the super-resolution convolutional neural network (SRCNN), deep learning technique has been widely applied in the field of image super-resolution. Previous works mainly focus on optimizing the structure of SRCNN, which have been achieved well performance in speed and restoration quality for image super-resolution. However, most of these approaches only consider a specific scale image during the training process, while ignoring the relationship between different scales of images. Motivated by this concern, in this paper, we propose a cascaded convolution neural network for image super-resolution (CSRCNN), which includes three cascaded Fast SRCNNs and each Fast SRCNN can process a specific scale image. Images of different scales can be trained simultaneously and the learned network can make full use of the information resided in different scales of images. Extensive experiments show that our network can achieve well performance for image SR.

[1]  Xiaogang Wang,et al.  Shape and Appearance Context Modeling , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[2]  Weiguo Gong,et al.  Combining sparse representation and local rank constraint for single image super resolution , 2015, Inf. Sci..

[3]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[4]  Madad Ali Shah,et al.  Single image super-resolution by directionally structured coupled dictionary learning , 2016, EURASIP J. Image Video Process..

[5]  Zhongfei Zhang,et al.  Semantics-Aware Deep Correspondence Structure Learning for Robust Person Re-Identification , 2016, IJCAI.

[6]  Hai Tao,et al.  Evaluating Appearance Models for Recognition, Reacquisition, and Tracking , 2007 .

[7]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Narendra Ahuja,et al.  Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Mahmoud Nazzal,et al.  Single image super resolution based on sparse representation via directionally structured dictionaries , 2014, 2014 22nd Signal Processing and Communications Applications Conference (SIU).

[10]  Jun-Hyuk Kim,et al.  Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality , 2018, Neurocomputing.

[11]  Thomas S. Huang,et al.  Deeply Improved Sparse Coding for Image Super-Resolution , 2015, ArXiv.

[12]  Cheng Shi,et al.  Adaptive multi-scale deep neural networks with perceptual loss for panchromatic and multispectral images classification , 2019, Inf. Sci..

[13]  Baowen Xu,et al.  Super-resolution Person re-identification with semi-coupled low-rank discriminant dictionary learning , 2015, CVPR.

[14]  Yang Wang,et al.  Single image super-resolution reconstruction based on genetic algorithm and regularization prior model , 2016, Inf. Sci..

[15]  Hamido Fujita,et al.  Computer Aided detection for fibrillations and flutters using deep convolutional neural network , 2019, Inf. Sci..

[16]  Shiguang Shan,et al.  Deep Network Cascade for Image Super-resolution , 2014, ECCV.

[17]  Jin Young Choi,et al.  Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  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).

[19]  Weiguo Gong,et al.  Dual-sparsity regularized sparse representation for single image super-resolution , 2015, Inf. Sci..

[20]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Yuhua Peng,et al.  Super-resolution Reconstruction Using Multiconnection Deep Residual Network Combined an Improved Loss Function for Single-frame Image , 2019, Multimedia Tools and Applications.

[22]  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.

[23]  Thomas S. Huang,et al.  Coupled Dictionary Training for Image Super-Resolution , 2012, IEEE Transactions on Image Processing.

[24]  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).

[25]  Quansen Sun,et al.  Optimal Couple Projections for Domain Adaptive Sparse Representation-Based Classification , 2017, IEEE Transactions on Image Processing.

[26]  Gang Wang,et al.  Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[27]  Baihua Xiao,et al.  Multi-Kernel Coupled Projections for Domain Adaptive Dictionary Learning , 2019, IEEE Transactions on Multimedia.

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

[29]  Shengcai Liao,et al.  Person re-identification by Local Maximal Occurrence representation and metric learning , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Wen Gao,et al.  Local patch encoding-based method for single image super-resolution , 2018, Inf. Sci..

[31]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[32]  Reinhard Klette,et al.  Coupled dictionary learning in wavelet domain for Single-Image Super-Resolution , 2018, Signal Image Video Process..

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

[34]  Siyuan Liu,et al.  Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[35]  Qiong Yan,et al.  Cascade Residual Learning: A Two-Stage Convolutional Neural Network for Stereo Matching , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[36]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[37]  Jian Huang,et al.  Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression , 2015, 1509.02957.

[38]  Luc Van Gool,et al.  Anchored Neighborhood Regression for Fast Example-Based Super-Resolution , 2013, 2013 IEEE International Conference on Computer Vision.

[39]  Luc Van Gool,et al.  A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution , 2014, ACCV.

[40]  Wei Wang,et al.  Transfer robust sparse coding based on graph and joint distribution adaption for image representation , 2018, Knowl. Based Syst..

[41]  Jingdong Wang,et al.  Deeply-Learned Part-Aligned Representations for Person Re-identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[42]  Said Raghay,et al.  A new multiframe super-resolution based on nonlinear registration and a spatially weighted regularization , 2019, Inf. Sci..

[43]  Liang Lin,et al.  Crafting a Toolchain for Image Restoration by Deep Reinforcement Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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

[45]  Yu Qiao,et al.  RankSRGAN: Generative Adversarial Networks With Ranker for Image Super-Resolution , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

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

[47]  Wei-Shi Zheng,et al.  Cross-View Asymmetric Metric Learning for Unsupervised Person Re-Identification , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[48]  Muhammad Waqas,et al.  Selective sparse coding based coupled dictionary learning algorithm for single image super-resolution , 2018, 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET).

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

[50]  Yuhui Zheng,et al.  Optimal Discriminative Projection for Sparse Representation-Based Classification via Bilevel Optimization , 2020, IEEE Transactions on Circuits and Systems for Video Technology.

[51]  Thomas S. Huang,et al.  Image super-resolution as sparse representation of raw image patches , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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

[53]  Kai Liu,et al.  Multiple Regressions based Image Super-resolution , 2019, Multimedia Tools and Applications.

[54]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[55]  Weifeng Liu,et al.  Canonical correlation analysis networks for two-view image recognition , 2017, Inf. Sci..

[56]  Steven C. H. Hoi,et al.  Deep Learning for Image Super-Resolution: A Survey , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[57]  Chih-Yuan Yang,et al.  Fast Direct Super-Resolution by Simple Functions , 2013, 2013 IEEE International Conference on Computer Vision.

[58]  Yu-Chiang Frank Wang,et al.  Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-Identification , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[59]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[60]  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).

[61]  Yuhui Zheng,et al.  Domain adaptive collaborative representation based classification , 2018, Multimedia Tools and Applications.

[62]  Yu Qiao,et al.  ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks , 2018, ECCV Workshops.

[63]  Le Zhang,et al.  A survey of randomized algorithms for training neural networks , 2016, Inf. Sci..