UPDCNN: A New Scheme for Image Upsampling and Deblurring Using a Deep Convolutional Neural Network

Restoration of a blurred and subsampled image is an ill-posed problem. In this paper, a two-stage convolutional network is proposed to carry out the processes of upsampling and deblurring to restore the original image. The main idea in the proposed scheme is that the deblurring process is attempted on a high PSNR image obtained after removing the ringing effect that is necessarily caused by the upsampling process. The evaluation of the proposed scheme is carried out using a benchmark dataset in terms of PSNR. The scheme is shown to outperform the state-of-the-art schemes, namely, the sparse coding network, the non-local means filters and the centralized sparse representation.

[1]  Ming-Hsuan Yang,et al.  Learning to Deblur Images with Exemplars , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Yi Wang,et al.  Scale-Recurrent Network for Deep Image Deblurring , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[3]  Ming-Hsuan Yang,et al.  Blind Image Deblurring via Deep Discriminative Priors , 2019, International Journal of Computer Vision.

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

[5]  Ce Liu,et al.  Deep Convolutional Neural Network for Image Deconvolution , 2014, NIPS.

[6]  Tae Hyun Kim,et al.  Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[8]  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).

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

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

[11]  Lei Zhang,et al.  Centralized sparse representation for image restoration , 2011, 2011 International Conference on Computer Vision.

[12]  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.

[13]  Thomas S. Huang,et al.  Learning a Mixture of Deep Networks for Single Image Super-Resolution , 2016, ACCV.

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

[15]  Xuelong Li,et al.  Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression , 2012, IEEE Transactions on Image Processing.

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

[17]  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).

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

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