Artifacts Reduction for Compression Image with Pyramid Residual Convolutional Neural Network

A pyramid residual convolutional neural network (PRCNN) is proposed to restore image with compression artifacts in this paper, and the method is enable to process multiple resolutions by fixing patch size extracted from whole image. Convolutional neural network has reached great performance in image processing (e.g. denoise, deblur, super-resolution), however deeper network may cause vanishing or exploding gradient problems, and it is hard to apply in realistic scene for high complexity. Thus, the residual blocks (RB) are proposed to balance between performance and application, besides, this paper exploits pyramid convolutional neural network to learn coarse-fine feature. In order to handle various resolutions, the fixed patch based method is used to adapt realistic scene. The experiment shows that the proposed algorithm can reduce compression artifacts through objective and subjective assessment, and the training/testing data are collected with H.264 coding. The proposed method can improve PSNR and SSIM from 0.54dB to 1.41dB, 0.01 to 0.04 while compression artifacts are reduced in visual quality, respectively.

[1]  Shiwen Shen,et al.  Adaptive non-local means filtering for image deblocking , 2011 .

[2]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[3]  Gregory Shakhnarovich,et al.  Deep Back-Projection Networks for Super-Resolution , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4]  Ajay Luthra,et al.  Overview of the H.264/AVC video coding standard , 2003, IEEE Trans. Circuits Syst. Video Technol..

[5]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1991, CACM.

[6]  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).

[7]  Liang Lin,et al.  Multi-level Wavelet-CNN for Image Restoration , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[8]  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).

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

[10]  Rui Sun,et al.  S-Net: a scalable convolutional neural network for JPEG compression artifact reduction , 2018, J. Electronic Imaging.

[11]  Chen Change Loy,et al.  EDVR: Video Restoration With Enhanced Deformable Convolutional Networks , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[12]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

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

[14]  Luca Benini,et al.  CAS-CNN: A deep convolutional neural network for image compression artifact suppression , 2016, 2017 International Joint Conference on Neural Networks (IJCNN).

[15]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[16]  Ali Mousavi,et al.  Learning to invert: Signal recovery via Deep Convolutional Networks , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

[18]  Pavan K. Turaga,et al.  ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  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).

[20]  Licheng Jiao,et al.  Image deblocking via sparse representation , 2012, Signal Process. Image Commun..

[21]  Antti Hallapuro,et al.  A Compression Objective and a Cycle Loss for Neural Image Compression , 2019, CVPR Workshops.

[22]  Xiaoou Tang,et al.  Compression Artifacts Reduction by a Deep Convolutional Network , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[23]  Ying Fu,et al.  Image Restoration from Patch-based Compressed Sensing Measurement , 2019, Neurocomputing.

[24]  Xiaoou Tang,et al.  LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.