Multiple-image super resolution using both reconstruction optimization and deep neural network

We present an efficient multi-image super resolution (MISR) method. Our solution consists of a L1-norm optimized reconstruction scheme for super resolution (SR), and a three-layer convolutional network for artifacts removal, in a concatenated fashion. Such a two-stage method achieves excellent performance, which outperforms the existing state-of-the-art SR methods in both subjective and objective measurements (e.g., 5 to 7 dB improvements on popular image database using PSNR metric).

[1]  Mei Han,et al.  Soft Edge Smoothness Prior for Alpha Channel Super Resolution , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

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

[3]  Renjie Liao,et al.  Video Super-Resolution via Deep Draft-Ensemble Learning , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

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

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

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

[7]  Luc Van Gool,et al.  Jointly Optimized Regressors for Image Super‐resolution , 2015, Comput. Graph. Forum.

[8]  Raanan Fattal,et al.  Image and video upscaling from local self-examples , 2011, TOGS.

[9]  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).

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

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

[12]  Xiaogang Wang,et al.  Image Transformation Based on Learning Dictionaries across Image Spaces , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[15]  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).

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

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

[18]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[19]  Yin Zhang,et al.  A Fast Algorithm for Sparse Reconstruction Based on Shrinkage, Subspace Optimization, and Continuation , 2010, SIAM J. Sci. Comput..

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

[21]  Enhua Wu,et al.  Handling motion blur in multi-frame super-resolution , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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