Deep Generative Adversarial Compression Artifact Removal

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2017-October, Issue , 2017, Pages 4836-4845

  Galteri, Leonardo ^a   Seidenari, Lorenzo ^a   Bertini, Marco ^a   Bimbo, Alberto Del ^a  

^a UNIVERSITY OF FLORENCE   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; MEAN SQUARE ERROR; OBJECT DETECTION; OBJECT RECOGNITION;

COMPRESSION ARTIFACTS; COMPUTER VISION ALGORITHMS; LOSSY COMPRESSIONS; MEAN SQUARED ERROR; NETWORK MODELING; OBJECT DETECTORS; PRE-PROCESSING STEP; STRUCTURAL SIMILARITY;

IMAGE COMPRESSION;

EID: 85041921723     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2017.517     Document Type: Conference Paper

References (41)

1. J. Bruna, P. Sprechmann, and Y. LeCun. Superresolution with deep convolutional sufficient statistics. CoRR, abs/1511.05666, 2015.
2. H. Chang, M. K. Ng, and T. Zeng. Reducing artifacts in JPEG decompression via a learned dictionary. IEEE Transactions on Signal Processing, 62(3):718-728, Feb 2014.
3. R. Dahl, M. Norouzi, and J. Shlens. Pixel Recursive Super Resolution. ArXiv preprint arXiv:1702.00783, Feb. 2017.
4. Y. Dar, A. M. Bruckstein, M. Elad, and R. Giryes. Postprocessing of compressed images via sequential denoising. IEEE Transactions on Image Processing, 25(7):3044-3058, July 2016.
5. C. Dong, Y. Deng, C. Change Loy, and X. Tang. Compression artifacts reduction by a deep convolutional network. In Proc. of IEEE International Conference on Computer Vision (ICCV), pages 576-584, 2015.
6. A. Dosovitskiy and T. Brox. Generating images with perceptual similarity metrics based on deep networks. In Advances in Neural Information Processing Systems (NIPS), 2016.
7. M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The PASCAL visual object classes challenge: A retrospective. International Journal of Computer Vision, 111(1):98-136, 2015.
8. A. Foi, V. Katkovnik, and K. Egiazarian. Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images. IEEE Transactions on Image Processing, 16(5):1395-1411, 2007.
9. L. A. Gatys, A. S. Ecker, and M. Bethge. Texture synthesis and the controlled generation of natural stimuli using convolutional neural networks. CoRR, abs/1505.07376, 2015.
10. L. A. Gatys, A. S. Ecker, and M. Bethge. Image style transfer using convolutional neural networks. In Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pages 2414-2423, 2016.
11. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems (NIPS), pages 2672-2680, 2014.
12. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016.
13. ITU. Rec. ITU-R BT.500-13-Methodology for the subjective assessment of the quality of television pictures, 2012.
14. J. Johnson, A. Alahi, and L. Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In Proc. of European Conference on Computer Vision (ECCV), pages 694-711, 2016.
15. J. Kim, J. Kwon Lee, and K. Mu Lee. Accurate image super-resolution using very deep convolutional networks. In Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pages 1646-1654, 2016.
16. D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. In Proc. of International Conference on Learning Representations (ICLR), 2015.
17. C. Ledig, L. Theis, F. Huszar, J. Caballero, A. P. Aitken, A. Tejani, J. Totz, Z.Wang, andW. Shi. Photorealistic single image super-resolution using a generative adversarial network. CoRR, abs/1609.04802, 2016.
18. Y. Li, F. Guo, R. T. Tan, and M. S. Brown. A contrast enhancement framework with JPEG artifacts suppression. In Proc. of European Conference on Computer Vision (ECCV), pages 174-188, Cham, 2014. Springer International Publishing. 2
19. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, and C. L. Zitnick. Microsoft COCO: Common objects in context. In Proc. of European Conference on Computer Vision (ECCV), pages 740-755. Springer, 2014.
20. H. Liu, R. Xiong, J. Zhang, and W. Gao. Image denoising via adaptive soft-thresholding based on nonlocal samples. In Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
21. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pages 3431-3440, 2015.
22. X. Mao, C. Shen, and Y.-B. Yang. Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. In Advances in Neural Information Processing Systems (NIPS), pages 2802-2810, 2016.
23. D. Martin, C. Fowlkes, D. Tal, and J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Proc. of IEEE International Conference on Computer Vision (ICCV), volume 2, pages 416-423. IEEE, 2001.
24. M. Mirza and S. Osindero. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784, 2014.
25. M. Nuutinen, T. Virtanen, O. Rummukainen, and J. Hakkinen. VQone MATLAB toolbox: A graphical experiment builder for image and video quality evaluations. Behavior Research Methods, 48(1):138-150, 2016.
26. A. Odena, V. Dumoulin, and C. Olah. Deconvolution and checkerboard artifacts. Distill, 2016. http://distill.pub/2016/deconv-checkerboard. 4
27. S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems (NIPS), pages 91-99, 2015.
28. T. Salimans, I. J. Goodfellow,W. Zaremba, V. Cheung, A. Radford, and X. Chen. Improved techniques for training GANs. CoRR, abs/1606.03498, 2016.
29. H. R. Sheikh, Z. Wang, L. Cormack, and A. C. Bovik. LIVE Image Quality Assessment Database Release 2, Apr. 2014.
30. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In Proc. of International Conference on Learning Representations (ICLR), 2015.
31. P. Svoboda, M. Hradis, D. Barina, and P. Zemcik. Compression artifacts removal using convolutional neural networks. arXiv preprint arXiv:1605.00366, 2016.
32. Z. Wang, A. C. Bovik, and L. Lu. Why is image quality assessment so difficult? In Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2002.
33. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004.
34. Z.Wang, D. Liu, S. Chang, Q. Ling, Y. Yang, and T. S. Huang. D3: Deep dual-domain based fast restoration of jpeg-compressed images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2764-2772, 2016.
35. S. Winkler. On the properties of subjective ratings in video quality experiments. In Proc. of Quality of Multimedia Experience (QME), 2009.
36. T.-S. Wong, C. A. Bouman, I. Pollak, and Z. Fan. A document image model and estimation algorithm for optimized JPEG decompression. IEEE Transactions on Image Processing, 18(11):2518-2535, 2009.
37. S. Yang, S. Kittitornkun, Y.-H. Hu, T. Q. Nguyen, and D. L. Tull. Blocking artifact free inverse discrete cosine transform. In Proc. of International Conference on Image Processing (ICIP), volume 3, pages 869-872. IEEE, 2000.
38. C. Yim and A. C. Bovik. Quality assessment of deblocked images. IEEE Transactions on Image Processing, 20(1):88-98, 2011.
39. J. Zhang, R. Xiong, C. Zhao, Y. Zhang, S. Ma, and W. Gao. CONCOLOR: Constrained non-convex lowrank model for image deblocking. IEEE Transactions on Image Processing, 25(3):1246-1259, March 2016.
40. K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang. Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing, to appear. 1, 2
41. X. Zhang, R. Xiong, X. Fan, S. Ma, and W. Gao. Compression artifact reduction by overlapped-block transform coefficient estimation with block similarity. IEEE Transactions on Image Processing, 22(12):4613-4626, 2013.