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JBE, vol. 28, no. 2, pp.185-193, March, 2023
DOI: https://doi.org/10.5909/JBE.2023.28.2.185 HDR Video Reconstruction via Content-based Alignment Network Haesoo Chung and Nam Ik Cho C.A E-mail: nicho@snu.ac.kr Abstract: As many different over-the-top (OTT) services become ubiquitous, demands for high-quality content are increasing. However,
high dynamic range (HDR) contents, which can provide more realistic scenes, are still insufficient. In this regard, we propose a
new HDR video reconstruction technique using multi-exposure low dynamic range (LDR) videos. First, we align a reference and
its neighboring frames to compensate for motions between them. In the alignment stage, we perform content-based alignment to
improve accuracy, and we also present a high-resolution (HR) module to enhance details. Then, we merge the aligned features to
generate a final HDR frame. Experimental results demonstrate that our method outperforms existing methods. Keyword: Image processing, Video, HDR Copyright 2023 Korean Institute of Broadcast and Media Engineers. All rights reserved. “This is an Open-Access article distributed under the terms of the Creative Commons BY-NC-ND (http://creativecommons.org/licenses/by-nc-nd/ Reference: [1] T. Grosch, “Fast and robust high dynamic range image generation with camera and object movement,” Vision, Modeling and Visualization, RWTH Aachen, pp.277-284, 2006. [2] S. Raman and S. Chaudhuri, “Reconstruction of high contrast images for dynamic scenes,” The Visual Computer, Vol.27, No.12, pp.1099 1114, 2011. doi: https://doi.org/10.1007/s00371-011-0653-0 [3] Y. S. Heo, K. M. Lee, S. U. Lee, Y. Moon, and J. Cha, “Ghost-free high dynamic range imaging,” Asian Conference on Computer Vision, Berlin, Heidelberg, pp.486 500, 2010. [4] N. K. Kalantari and R. Ramamoorthi, “Deep high dynamic range imaging of dynamic scenes,” ACM Trans. Graph., Vol.36, No.4, pp.144 1, 2017. doi: http://dx.doi.org/10.1145/3072959.3073609 [5] K. R. Prabhakar, G. Senthil, S. Agrawal, R. V. Babu, and R. K. S. S. Gorthi, “Labeled from unlabeled: Exploiting unlabeled data for few-shot deep hdr deghosting,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.4875 4885, 2021. [6] Y. Niu, J. Wu, W. Liu, W. Guo, and R. WH Lau, “Hdr-gan: Hdr image reconstruction from multi-exposed ldr images with large motions,” IEEE Transactionson Image Processing, pp.3885 3896, 2021. doi: https://doi.org/10.1109/TIP.2021.3064433 [7] Z. Pu, P. Guo, M. S. Asif, and Z. Ma, “Robust high dynamic range (hdr) imaging with complex motion and parallax,” Proceedings of the Asian Conference on Computer Vision, 2020. [8] Q. Yan, D. Gong, Q. Shi, A. V. D. Hengel, C. Shen, I. Reid, and Y. Zhang, “Attention-guided network for ghost-free high dynamic range imaging,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.1751-1760, 2019. [9] N. K. Kalantari, E. Shechtman, C. Barnes, S. Darabi, D. B. Goldman, and P. Sen, “Patch-based high dynamic range video,” ACM Trans. Graph., Vol.32, No.6, pp.202-1, 2013. doi: https://doi.org/10.1145/2508363.2508402 [10] N. K. Kalantari and R. Ramamoorthi, “Deep HDR video from sequences with alternating exposures,” Computer Graphics Forum, Vol.38, No.2, pp.193-205, 2019. doi: https://doi.org/10.1111/cgf.13630 [11] G. Chen, C. Chen, S. Guo, Z. Liang, K. Y. K. Wong, L. Zhang, “HDR video reconstruction: A coarse-to-fine network and a real-world benchmark dataset,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.2502-2511, 2021. [12] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.770-778, 2016. [13] X. Mao, Y. Liu, W. Shen, Q. Li, and Y. Wang, “Deep residual fourier transformation for single image deblurring,” arXiv preprint arXiv:2111.11745, 2021. [14] T. Xue, B. Chen, J. Wu, D. Wei, and W. T. Freeman, “Video enhancement with task-oriented flow,” International Journal of Computer Vision, Vol.127, No.8, pp.1106-1125, 2019. doi: https://doi.org/10.1007/s11263-018-01144-2 Comment |
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