Abstract
In many practical scenarios, the images to be super-resolved are not only of low resolution (LR) but also JPEG compressed, while most of the existing super-resolution methods assume compression free LR image inputs. As a result, the JPEG compression artifacts (e.g., blocking artifacts) are often exacerbated in the super-resolved images, leading to unpleasant visual results. In this paper, we address this problem via learning a deep residual convolutional neural network (CNN) that exploits a skips-in-skip connection. More specifically, by increasing the network depth to 31 layers with receptive field of 63 by 63, we train a single CNN model which is able to handle JPEG image super-resolution with various combinations of scale and quality factors, as well as the extreme cases, i.e., image super-resolution with multiple scale factors, and JPEG image deblocking with different quality factors. Our extensive experimental results demonstrate that the proposed deep model can not only yield high resolution (HR) images that are visually more pleasant than those state-of-the-art deblocking and super-resolution methods in a cascaded manner, but also deliver very competitive results with the state-of-the-art super-resolution methods and JPEG deblocking methods in terms of quantitative and qualitative measures.
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References
Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: CVPR, pp. 5197–5206. IEEE (2015)
Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE TIP 19, 2861–2873 (2010)
Timofte, R., De Smet, V., Van Gool, L.: A+: adjusted anchored neighborhood regression for fast super-resolution. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9006, pp. 111–126. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16817-3_8
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_13
Xiong, Z., Sun, X., Wu, F.: Robust web image/video super-resolution. IEEE TIP 19, 2017–2028 (2010)
Kang, L.W., Hsu, C.-C., Zhuang, B., Lin, C.-W., Yeh, C.-H.: Learning-based joint super-resolution and deblocking for a highly compressed image. IEEE Trans. Multimed. 17, 921–934 (2015)
Singh, A., Porikli, F., Ahuja, N.: Super-resolving noisy images. In: CVPR, pp. 2846–2853 (2014)
LeCun, Y., Kavukcuoglu, K., Farabet, C., et al.: Convolutional networks and applications in vision. In: ISCAS, pp. 253–256 (2010)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)
Xu, L., Ren, J.S., Liu, C., Jia, J.: Deep convolutional neural network for image deconvolution. In: NIPS, pp. 1790–1798 (2014)
Bruna, J., Sprechmann, P., LeCun, Y.: Super-resolution with deep convolutional sufficient statistics, arXiv preprint arXiv:1511.05666 (2015)
Dong, C., Deng, Y., Loy, C.C., Tang, X.: Compression artifacts reduction by a deep convolutional network. In: ICCV, pp. 576–584 (2015)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)
Rothe, R., Timofte, R., Van Gool, L.: Efficient regression priors for reducing image compression artifacts. In: ICIP, pp. 1543–1547 (2015)
Kwon, Y., Kim, K.I., Tompkin, J., Kim, J.H., Theobalt, C.: Efficient learning of image super-resolution and compression artifact removal with semi-local gaussian processes. IEEE TPAMI 37, 1792–1805 (2015)
Chen, Y., Pock, T.: Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration, arXiv preprint arXiv:1508.02848 (2015)
Kim, J., Lee, J.K., Lee, J.K.: Accurate image super-resolution using very deep convolutional networks, arXiv preprint arXiv:1511.04587 (2015)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift, arXiv preprint arXiv:1502.03167 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition, arXiv preprint arXiv:1512.03385 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks, arXiv preprint arXiv:1603.05027 (2016)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: CVPR, pp. 1–9 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: ICCV, pp. 1026–1034 (2015)
Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE TPAMI 38, 295–307 (2016)
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Xu, F., Yan, Z., Xiao, G., Zhang, K., Zuo, W. (2018). JPEG Image Super-Resolution via Deep Residual Network. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_50
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