An Efficient Super-Resolution Network Based on Aggregated Residual Transformations

In this paper, we propose an efficient multibranch residual network for single image super-resolution. Based on the idea of aggregated transformations, the split-transform-merge strategy is exploited to implement the multibranch architecture in an easy, extensible way. By this means, both the number of parameters and the time complexity are significantly reduced. In addition, to ensure the high-performance of super-resolution reconstruction, the residual block is modified and simplified with reference to the enhanced deep super-resolution network (EDSR) model. Moreover, our developed method possesses advantages of flexibility and extendibility, which are helpful to establish a specific network according to practical demands. Experimental results on both the Diverse 2K (DIV2K) and other standard datasets show that the proposed method can achieve a good performance in comparison with EDSR under the same number of convolution layers.

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

[2]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[3]  Babak Hassibi,et al.  Second Order Derivatives for Network Pruning: Optimal Brain Surgeon , 1992, NIPS.

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

[5]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[6]  J. Dudczyk,et al.  A method of feature selection in the aspect of specific identification of radar signals , 2017 .

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

[8]  Peter M. Atkinson,et al.  Sub‐pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using super‐resolution pixel‐swapping , 2006 .

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

[10]  J. Dudczyk,et al.  Adaptive Forming of the Beam Pattern of Microstrip Antenna with the Use of an Artificial Neural Network , 2012 .

[11]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

[12]  Jan Matuszewski,et al.  Neural network application for emitter identification , 2017, 2017 18th International Radar Symposium (IRS).

[13]  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).

[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]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

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

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

[18]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[19]  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).

[20]  Jan Matuszewski,et al.  Objects detection and recognition system using artificial neural networks and drones , 2017, 2017 Signal Processing Symposium (SPSympo).

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

[22]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Mohammed Ghanbari,et al.  Scope of validity of PSNR in image/video quality assessment , 2008 .

[24]  D. Yeung,et al.  Super-resolution through neighbor embedding , 2004, CVPR 2004.

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

[26]  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.

[27]  Varsha H. Patil.  Super Resolution in Medical Imaging for An Automated Proficient Process , 2006 .

[28]  Xuelong Li,et al.  Image Super-Resolution With Sparse Neighbor Embedding , 2012, IEEE Transactions on Image Processing.

[29]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[30]  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).