Abstract
Image inpainting is an extremely challenging and open problem for the computer vision community. Motivated by the recent advancement in deep learning algorithms for computer vision applications, we propose a new end-to-end deep learning based framework for image inpainting. Firstly, the images are down-sampled as it reduces the targeted area of inpainting therefore enabling better filling of the target region. A down-sampled image is inpainted using a trained deep convolutional auto-encoder (CAE). A coupled deep convolutional auto-encoder (CDCA) is also trained for natural image super resolution. The pre-trained weights from both of these networks serve as initial weights to an end-to-end framework during the fine tuning phase. Hence, the network is jointly optimized for both the aforementioned tasks while maintaining the local structure/information. We tested this proposed framework with various existing image inpainting datasets and it outperforms existing natural image blind inpainting algorithms. Our proposed framework also works well to get noise resilient super-resolution after fine-tuning on noise-free super-resolution dataset. It provides more visually plausible and better resultant image in comparison of other conventional and state-of-the-art noise-resilient super-resolution algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH (2000)
Guillemot, C., Le Meur, O.: Image inpainting: overview and recent advances. IEEE Signal Process. Mag. 31(1), 127–144 (2014)
Mao, X.-J., Shen, C., Yang, Y.-B.: Image Denoising Using Very Deep Fully Convolutional Encoder-Decoder Networks with Symmetric Skip Connections. CoRR (2016)
Cai, N., Su, Z., Lin, Z., Wang, H., Yang, Z., Ling, B.W.K.: Blind inpainting using the fully convolutional neural network. Vis. Comput. 33(2), 249–261 (2017)
Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: Advances in Neural Information Processing Systems, pp. 341–349 (2012)
Fawzi, A., Samulowitz, H., Turaga, D., Frossard, P.: Image inpainting through neural networks hallucinations. In: IEEE 12th Image, Video and Multidimensional Signal Processing Workshop, IVMSP (2016)
Le Meur, O., Ebdelli, M., Guillemot, C.: Hierarchical super-resolution-based inpainting. IEEE Trans. Image Process. 22(10), 3779–3790 (2013)
Sharma, M., Chaudhury, S., Lall, B.: Deep learning based frameworks for image super-resolution and noise-resilient super-resolution. In: International Joint Conference on Neural Networks, IJCNN (2017)
Mao, X.-J., Shen, C., Yang, Y.-B.: Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections. CoRR (2016)
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
Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate Image Super-Resolution Using Very Deep Convolutional Networks. CoRR (2015)
Wang, Z., Liu, D., Yang, J., Han, W., Huang, T.: Deep networks for image super-resolution with sparse prior. In: IEEE International Conference on Computer Vision, ICCV (2015)
Kim, J., Kwon Lee, J., Mu Lee, K.: Deeply-Recursive Convolutional Network for Image Super-Resolution. CoRR (2015)
Yang, W., Feng, J., Yang, J., Zhao, F., Liu, J., Guo, Z., Yan, S.: Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution. CoRR (2016)
Ledig, C., Theis, L., Huszar, F., Caballero, J., Aitken, A.P., Tejani, A., Totz, J., Wang, Z., Shi, W.: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial. CoRR (2016)
Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. CoRR (2016)
Criminisi, A., Perez, P., Toyama, K.: Region filling and object removal by exemplar-based image inpainting. IEEE Trans. Image Process. 13(9), 1200–1212 (2004)
Bertalmio, M., Bertozzi, A.L., Sapiro, G.: Navier-stokes, fluid dynamics, and image and video inpainting. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR (2001)
Ghorai, M., Mandal, S., Chanda, B.: Patch sparsity based image inpainting using local patch statistics and steering kernel descriptor. In: 23rd International Conference on Pattern Recognition, ICPR (2016)
Fadili, M.-J., Starck, J.-L., Murtagh, F.: Inpainting and zooming using sparse representations. Comput. J. 52(1), 64–79 (2009)
Le Meur, O., Gautier, J., Guillemot, C.: Examplar-based inpainting based on local geometry. In: 18th IEEE International Conference on Image Processing, ICIP (2011)
Dong, B., Ji, H., Li, J., Shen, Z., Xu, Y.: Wavelet frame based blind image inpainting. Appl. Comput. Harmon. Anal. 32(1), 268–279 (2012)
Liu, J., Musialski, P., Wonka, P., Ye, J.: Tensor completion for estimating missing values in visual data. IEEE Trans. Pattern Anal. Mach. Intell. TPAMI 35(1), 208–220 (2013)
Pathak, D., Krähenbühl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2016)
Yeh, R., Chen, C., Lim, T.-Y., Hasegawa-Johnson, M., Do, M.N.: Semantic Image Inpainting with Perceptual and Contextual Losses. CoRR (2016)
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. IJCV 115(3), 211–252 (2015)
Image Inpainting Dataset of Computational Intelligence Lab - ETH Zürich, ETH-CIL Dataset (2012)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: 8th International Conference on Computer Vision, ICCV (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sharma, M., Mukhopadhyay, R., Chaudhury, S., Lall, B. (2018). An End-to-End Deep Learning Framework for Super-Resolution Based Inpainting. In: Rameshan, R., Arora, C., Dutta Roy, S. (eds) Computer Vision, Pattern Recognition, Image Processing, and Graphics. NCVPRIPG 2017. Communications in Computer and Information Science, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-0020-2_18
Download citation
DOI: https://doi.org/10.1007/978-981-13-0020-2_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0019-6
Online ISBN: 978-981-13-0020-2
eBook Packages: Computer ScienceComputer Science (R0)