Abstract
Existing image inpainting algorithms based on neural network models are affected by structural distortions and blurred textures on visible connectivity. As a result, overfitting and overlearning phenomena can easily emerge during the image inpainting procedure. Image inpainting refers to the repairing of missing parts of an image, given an image that is broken or incomplete. After the repairing operation is complete, there are obvious signs of repair in damaged areas, semantic discontinuities, and unclearness. This paper proposes an improved image inpainting method based on a new encoder combined with a context loss function. In order to obtain clear repaired images and ensure that the semantic features of images are fully learned, a generative network based on the fusion model of squeeze-and-excitation networks deep residual learning has been proposed to improve the application of network features in order to obtain clear images and reduce network parameters. At the same time, a discriminative network based on the squeeze-and-excitation residual Network has been proposed to strengthen the capability of the discriminative network. In order to make the generated image more realistic, so that the restored image will be more similar to the original image, a joint context-awareness loss training method (contextual perception loss network) has also been proposed to generate the similarity of the local features of the network constraint, with the result that the repaired image is closer to the original picture and more realistic. The experimental results can demonstrate that the proposed algorithm demonstrates better adaptive capability than the comparison algorithms on a number of image categories. In addition, the processing results of the image inpainting procedure were also superior to those of five state-of-the-art algorithms.
Similar content being viewed by others
References
Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.: Patchmatch: a randomized correspondence algorithm for structural image editing. ACM Trans. Graph. 28(3), 24–34 (2009)
Chen, Y., Xiong, J., Xu, W., Zuo, J.: A novel online incremental and decremental learning algorithm based on variable support vector machine. Cluster Comput. 22, 7435–7445 (2019)
Wang, C., Chan, S., Zhu, Z., Zhang, L., Shum, H.: Superpixel-based color-depth restoration and dynamic environment modeling for Kinect-assisted image-based rendering systems. Vis. Comput. 34, 67–81 (2018)
Haouchine, N., Roy, F., Courtecuisse, H., Niebner, M., Cotidn, S.: Calipso: physics-based image and video editing through CAD model proxies. Vis. Comput. 36, 211–226 (2020)
Liu, B., Li, P., Sheng, B., Nie, Y., Wu, E.: Structure-preserving image completion with multi-level dynamic patches. Vis. Comput. 35, 85–98 (2019)
Zhao, H., Rosin, P., Lai, Y., Wang, Y.: Automatic semantic style transfer using deep convolutional neural networks and soft masks. Vis. Comput. 36, 1307–1324 (2020)
Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311–4322 (2006)
Zhang, J., Wu, Y., Feng, W., Wang, J.: Spatially attentive visual tracking using multi-model adaptive response fusion. IEEE Access 7, 83873–83887 (2019)
Chen, Y., Xu, W., Zuo, J., Yang, K.: The fire recognition algorithm using dynamic feature fusion and IV-SVM classifier. Cluster Comput. 22, 7665–7675 (2019)
Wang, J., Qin, J., Xiang, X., Tan, Y., Pan, N.: CAPTCHA recognition based on deep convolutional neural network. Math. Biosci. Eng. 16(5), 5851–5861 (2019)
Chen, Y., Tao, J., Liu, L., Xiong, J., Xia, R., Xie, J., Zhang, Q., Yang, K.: Research of improving semantic image segmentation based on a feature fusion model. J. Ambient Intell. Humaniz. Comput. (2020). https://doi.org/10.1007/s12652-020-02066-z
Zhang, J., Zhong, S., Wang, T., Chao, H., Wang, J.: Blockchain-based systems and applications: a survey. J. Internet Technol. 21(1), 1–14 (2020)
Altantawy, D., Saleh, A., Kishk, S.: Texture-guided depth upsampling using Bregman split: a clustering graph-based approach. Vis. Comput. 36, 333–359 (2020)
Yang, C., Feng, H., Xu, Z., Li, Q., Chen, Y.: Correction of overexposure utilizing haze removal model and image fusion technique. Vis. Comput. 35, 695–705 (2019)
Liu, Y., Pan, J., Su, Z., Tang, K.: Robust dense correspondence using deep convolutional features. Vis. Comput. 36, 827–841 (2020)
Yin, B., We, X., Wang, J., Xiong, N., Gu, K.: An industrial dynamic skyline based similarity joins for multi-dimensional big data applications. IEEE Trans. Ind. Inform. 16(4), 2520–2532 (2020)
Beckouche, S., Starck, J., Fadili, J.: Astronomical Image Denoising Using Dictionary Learning. (2013). arXiv arXiv:1304.3573
Hu, G., Ling, X.: Criminisi-based sparse representation for image inpainting. In: Proceedings of IEEE International Conference on Multimedia Big Data, Laguna Hills, CA, USA, 19–21 April 2017, pp. 389–393 (2017)
Darabi, S., Shechtman, E., Barnes, C., Goldman, D., Sen, P.: Image melding: combining inconsistent images using patch-based synthesis. ACM Trans. Graph. 31(4), 1–10 (2012)
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.: Context encoders: feature learning by inpainting. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016, pp. 2536–2544 (2016)
Yu, J., Zhe, L., Yang, J., Shen, X., Xin, L., Huang, T.: Generative image inpainting with contextual attention. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–22 June 2018, pp. 5505–5014 (2018)
Luo, Y., Qin, J., Xiang, X., Tan, Y., Liu, Q., Xiang, L.: Coverless real-time image information hiding based on image block matching and Dense convolutional network. J. Real-Time Image Process. 17(1), 125–135 (2020)
Naderahmadian, T., Beheshti, S., Ali, M.: Correlation based online dictionary learning algorithm. IEEE Trans. Signal Process. 64(3), 592–602 (2015)
Yang, H., Zhang, Z.: Depth image upsampling based on guided filter with low gradient minimization. Vis. Comput. 36, 1411–1422 (2020)
Li, W., Xu, H., Li, H., Yang, Y., Sharma, P., Wang, J., Singh, S.: Complexity and algorithms for superposed data uploading problem in networks with smart devices. IEEE Internet Things J. (2019). https://doi.org/10.1109/JIOT.2019.2949352
HaCohen, Y., Fattal, R., Lischinski, D.: Image upsampling via texture hallucination. In: Proceedings of International Conference on Computational Photography, Cambridge, MA, USA, 23 September 2010, pp. 1–8 (2010)
Liao, N., Song, Y., Huang, X., Wang, J.: Detection of probe flow anomalies using information entropy. J. Intell. Fuzzy. Syst. (2020). https://doi.org/10.3233/IFS-191448
Yu, F., Liu, L., Qian, S., Li, L., Huang, Y., Shi, C., Cai, S., Wu, X., Du, S., Wan, Q.: Chaos-based application of a novel multistable 5D memristive hyperchaotic system with coexisting multiple attractors. Complexity 2020, Article ID 8034196 (2020)
Sheng, G., Tang, X., Xie, K., Xiong, J.: Hydraulic fracturing microseismic first arrival picking method based on non-subsampled shearlet transform and higher-order-statistics. J. Seism. Explor. 28(6), 593–618 (2019)
Gu, K., Zhang, W., Lim, S., Sharma, P., Al-Makhadmeh, Z., Tolba, A.: Reusable mesh signature scheme for protecting identity privacy of IoT devices. Sensors 20, 758 (2020)
Liu, Y., Cheng, M., Fan, D., Zhang, L., Bian, J., Tao, D.: Semantic edge detection with diverse deep supervision (2018). arXiv arXiv:1804.02864v3
Liu, Y., Li, S., Cheng, M.: Refinedbox: refining for fewer and high-quality object proposals. Neurocomputing (2020). https://doi.org/10.1016/j.neucom.2020.04.017
Iizuka, S., Simo-Serra, E., Ishikawa, H.: Globally and locally consistent image completion. ACM Trans. Graph. 36(4), 107:1–107:14 (2017)
Yu, F., Liu, L., Shen, H., Zhang, Z., Huang, Y., Shi, C., Cai, S., Wu, X., Du, S., Wan, Q.: Dynamic analysis, circuit design and synchronization of a novel 6D memristive four-wing hyperchaotic system with multiple coexisting attractors. Complexity 2020, Article ID 5904607 (2020)
Chen, Y., Wang, J., Liu, S., Chen, X., Xiong, J., Xie, J., Yang, K.: Multiscale fast correlation filtering tracking algorithm based on a feature fusion model. Concurr. Comput. Pract. Exp. (2019). https://doi.org/10.1002/cpe.5533
Liao, Z., Peng, J., Chen, Y., Zhang, J., Wang, J.: A fast Q-learning based data storage optimization for low latency in data center networks. IEEE Access 8, 90630–90639 (2020)
Mikaeli, E., Aghagolzadeh, A., Azghani, M.: Single-image super-resolution via patch-based and group-based local smoothness modeling. Vis. Comput. (2019). https://doi.org/10.1007/s00371-019-01756-w
Nie, G., Cheng, M., Liu, Y., Liang, Z., Fan, D., Liu, Y., Wang, Y.: Multi-level context ultra-aggregation for stereo matching. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 16–20 June 2019, pp. 3283–3291 (2019)
Pan, N., Qin, J., Tan, Y., Xiang, X., Hou, G.: A video coverless information hiding algorithm based on semantic segmentation. EURASIP J. Image Video Process. (2020). https://doi.org/10.1186/s13640-020-00512-8
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–22 June 2018, pp. 7132–7141 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016, pp. 770–778 (2016)
Yang, C., Yang, M.: Fast direct super-resolution by simple functions. In: Proceedings of IEEE International Conference on Computer Vision, Sydney, Australia, 1–8 December 2013, pp. 561–568 (2013)
Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: Proceedings of International Conference on Pattern Recognition, Istanbul, Turkey, 23–26 August 2010, pp. 2366–2369 (2010)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Proceedings of Annual Conference on Neural Information Processing System, Montreal, Quebec, Canada, 7–12 December 2015, pp. 5672–2680 (2015)
Chen, Y., Wang, J., Xia, R., Zhang, Q., Cao, Z., Yang, K.: The visual object tracking algorithm research based on adaptive combination kernel. J. Ambient Intell. Humaniz. Comput. 10(12), 4855–4867 (2019)
Yu, F., Shen, H., Liu, L., Zhang, Z., Huang, Y., He, B., Cai, S., Song, Y., Yin, B., Du, S., Xu, Q.: CCII and FPGA realization: a multistable modified four-order autonomous Chua’s chaotic system with coexisting multiple attractors. Complexity 2020, Article ID 5212601 (2020)
Zhou, L., Zhang, T., Tian, Y., Huang, H.: Fraction-order total variation image blind restoration based on self-similarity features. IEEE Access 8, 30346–30444 (2020)
Zhang, J., Xie, Z., Sun, J., Zou, X., Wang, J.: A cascaded R-CNN with multiscale attention and imbalanced samples for traffic sign detection. IEEE Access 8, 29742–29754 (2020)
Li, Y., Liu, S., Yang, J., Yang, M.: Generative face completion. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017, pp. 5892–5900 (2017)
Yeh, A., Chen, C., Lim, T., Schwing, A., Hasegawa-Johnson, M.: Do M: Semantic image inpainting with deep generative models. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017, pp. 6882–6890 (2017)
Sheikh, H., Bovik, A., Veciana, G.: An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Process. 14(12), 2117–2128 (2005)
Zhang, J., Wang, W., Lu, C., Wang, J., Sangaiah, A.: Lightweight deep network for traffic sign classification. Ann. Telecommun. (2019). https://doi.org/10.1007/s12243-019-00731-9
Chen, Y., Wang, J., Chen, X., Zhu, M., Yang, K., Wang, Z., Xia, R.: Single-image super-resolution algorithm based on structural self-similarity and deformation block features. IEEE Access 7, 58791–58801 (2019)
Chen, Y., Tao, J., Zhang, Q., Yang, K., Chen, X., Xiong, J., Xia, R., Xie, J.: Saliency detection via improved hierarchical principle component analysis method. Wirel. Commun. Mob. Comput. 2020, Article ID 8822777 (2020)
Cheng, M., Liu, X., Wang, J., Lu, S.: Structure-preserving neural style transfer. IEEE Trans. Image Process. 29, 909–920 (2019)
Acknowledgements
We would like to thank Jin Wang, Jianming Zhang, Ke Gu, Fei Yu, and Shuo Cai for their help in this research. We are grateful to anonymous referees for useful comments and suggestions.
Funding
This study was funded by the National Natural Science Foundation of China [61972056, 61772454, 61402053, 61981340416], the Natural Science Foundation of Hunan Province of China [2020JJ4623], the Scientific Research Fund of Hunan Provincial Education Department [17A007, 19C0028, 19B005], the Changsha Science and Technology Planning [KQ1703018, KQ1706064, KQ1703018-01, KQ1703018-04], the Junior Faculty Development Program Project of Changsha University of Science and Technology [2019QJCZ011], the “Double First-class” International Cooperation and Development Scientific Research Project of Changsha University of Science and Technology [2019IC34], the Practical Innovation and Entrepreneurship Ability Improvement Plan for Professional Degree Postgraduate of Changsha University of Science and Technology [SJCX202072], and the Postgraduate Training Innovation Base Construction Project of Hunan Province [2019-248-51].
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chen, Y., Liu, L., Tao, J. et al. The improved image inpainting algorithm via encoder and similarity constraint. Vis Comput 37, 1691–1705 (2021). https://doi.org/10.1007/s00371-020-01932-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00371-020-01932-3