Abstract
The performances of most image registrations will decrease if the quality of the image to be registered is poor, especially contaminated with heavy distortions such as noise, blur, and uneven degradation. To solve this problem, a generative adversarial networks (GANs) based approach and the specified loss functions are proposed to improve image quality for better registration. Specifically, given the paired images, the generator network enhances the distorted image and the discriminator network compares the enhanced image with the ideal image. To efficiently discriminate the enhanced image, the loss function is designed to describe the perceptual loss and the adversarial loss, where the former measures the image similarity and the latter pushes the enhanced solution to natural image manifold. After enhancement, image features are more accurate and the registrations between feature point pairs will be more consistent.
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References
Zitováand, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21, 977–1000 (2003)
Barnea, D.I., Silverman, H.F.: A class of algorithms for fast digital image registration. IEEE Trans. Comput. 21, 179–186 (1972)
Guo, X., Xu, Z., Lu, Y., Pang, Y.: An application of Fourier–Mellin transform in image registration. In: Proceedings of the 5th International Conference on Computer and Information Technology (CIT), Shanghai, China, September 2005, pp. 619–623 (2005)
Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32
Dosovitskiy, A., Fischer, P., Springenberg, J.T., Riedmiller, M., Brox, T.: Discriminative unsupervised feature learning with exemplar convolutional neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(9), 1734–1747 (2016)
Yang, Z., Dan, T., Yang, Y.: Multi-temporal remote sensing image registration using deep convolutional features. IEEE Access 6, 38544–38555 (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
DeTone, D., Malisiewicz, T., Rabinovich, A.: Deep image homography estimation. arXiv preprint arXiv:1606.03798 (2016)
Liao, R., et al.: An artificial agent for robust image registration. In: AAAI, pp. 4168–4175 (2017)
Nguyen, T., Chen, S.W., Shivakumar, S.S., Taylor, C.J., Kumar, V.: Unsupervised deep homography: a fast and robust homography estimation model. arXiv preprint arXiv:1709.03966 (2017)
Divya, K.A., Roshna, K.I.: A survey on various image enhancement algorithms for naturalness preservation. Int. J. Comput. Sci. Inf. Technol. 6, 2043–2045 (2015)
Bedi, S., Khandelwal, R.: Various image enhancement techniques—a critical review. Int. J. Adv. Res. Comput. Commun. Eng. 2(3), 1605–1609 (2013)
Yang, F., Wu, J.: An improved image contrast enhancement in multiple-peak images based on histogram equalization. In: 2010 International Conference on Computer Design and Applications, vol. 1, no. 4, pp. 346–349. IEEE (2010)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Ignatov, A., Kobyshev, N., Vanhoey, K., Timofte, R., Van Gool, L.: DSLR-quality photos on mobile devices with deep convolutional networks. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), October 2017
Chen, Y.-S., Wang, Y.-C., Kao, M.-H., Chuang, Y.-Y.: Deep photo enhancer: unpaired learning for image enhancement from photographs with GANs. In: Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, pp. 6306–6314 (2018)
Huang, J., et al.: Range scaling global U-net for perceptual image enhancement on mobile devices. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 230–242. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_15
Tripathi, S., Lipton, Z.C., Nguyen, T.Q.: Correction by projection: denoising images with generative adversarial networks. arXiv:1803.04477 (2018)
Chen, J., Chen, J., Chao, H., Yang, M.: Image blind denoising with generative adversarial network-based noise modeling. In: Proceedings of the IEEE International Conference Computer Vision Pattern Recognition, pp. 3155–3164, June 2018
Meng, Y., Kong, D., Zhu, Z., Zhao, Y.: From night to day: GANs based low quality image enhancement. Neural Process. Lett. 50(1), 799–814 (2019). https://doi.org/10.1007/s11063-018-09968-2
Jiang, Y., et al.: Enlightengan: deep light enhancement without paired supervision. arXiv:1906.06972 (2019)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv preprint arXiv:1701.07875 (2017)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of Wasserstein GANs.arXiv e-prints arXiv:1704.00028 (2017). Advances in Neural Information Processing Systems 31 (NIPS 2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR) (2015)
Aly, H.A., Dubois, E.: Image up-sampling using total variation regularization with a new observation model. IEEE Trans. Image Process. 14(10), 1647–1659 (2005)
Vedaldi, A., Fulkerson, B.: VLFeat: an open and portable library of computer vision algorithms (2008)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR, abs/1412.6980 (2014)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Krishnan, D., Tay, T., Fergus, R.: Blind deconvolution using a normalized sparsity measure. In: CVPR, pp. 233–240 (2011)
Pan, J., Hu, Z., Su, Z., Yang, M.-H.: Deblurring text imagesvia L0-regularized intensity and gradient prior. In: CVPR, pp. 2901–2908 (2014)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: an efficient alternative to SIFT or SURF. In: Proceedings of the IEEE International Conference on Computer Vision, Barcelona, Spain, November 2011, pp. 2564–2571 (2011)
Calonder, M., Lepetit, V., Özuysal, M., Trzinski, T., Strecha, C., Fua, P.: BRIEF: computing a local binary descriptor very fast. IEEE Trans. Pattern Anal. Machine Intell. 34(7), 1281–1298 (2011)
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Jiang, S., Wang, C., Huang, C. (2021). Image Registration Improved by Generative Adversarial Networks. In: Lokoč, J., et al. MultiMedia Modeling. MMM 2021. Lecture Notes in Computer Science(), vol 12573. Springer, Cham. https://doi.org/10.1007/978-3-030-67835-7_3
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DOI: https://doi.org/10.1007/978-3-030-67835-7_3
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