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High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision.
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2021
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L. Wang, Y. Chae, S.-H. Yoon, T.-K. Kim, and K.-J. Yoon, “Evdistill: Asynchronous events to end-task learning via bidirectional reconstruction-guided cross-modal knowledge distillation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 608–619
2021
Closest in time.
X. Deng, Y. Zhang, M. Xu, S. Gu, and Y. Duan, “Deep coupled feedback network for joint exposure fusion and image super-resolution,” TIP , vol. 30, pp. 3098–3112, 2021
2021
Closest in time.
X. Chen, Y. Liu, Z. Zhang, Y. Qiao, and C. Dong, “Hdrunet: Single image hdr reconstruction with denoising and dequantization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop , 2021, pp. 354–363
2021
Closest in time.
J. Koh, J. Lee, and S. Yoon, “Single-image deblurring with neural networks: A comparative survey,” CVIU , 2021
2021
Closest in time.
M. Mostafavi, L. Wang, and K.-J. Yoon, “Learning to reconstruct hdr images from events, with applications to depth and flow prediction,” IJCV , pp. 1–21, 2021
2021
Closest in time.
F. Paredes-Vallés and G. C. de Croon, “Back to event basics: Self-supervised learning of image reconstruction for event cameras via photometric constancy,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021
2021
Closest in time.
L. Wang, T.-K. Kim, and K.-J. Yoon, “Joint framework for single image reconstruction and super-resolution with an event camera,” TPAMI , no. 01, pp. 1–1, 2021
2021
Closest in time.
X. Zhang, L. Wei, L. Yu, W. Yang, and G.-S. Xia, “Event-based synthetic aperture imaging with a hybrid network,” arXiv e-prints , 2021
2021
Closest in time.
L. Wang, Y. Chae, and K.-J. Yoon, “Dual transfer learning for event-based end-task prediction via pluggable event to image translation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 2135–2145
2021
Closest in time.
Z. Chen, Q. Zheng, P. Niu, H. Tang, and G. Pan, “Indoor lighting estimation using an event camera,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 14 760–14 770
2021
Closest in time.
S. Goswami and S. K. Singh, “A novel registration & colorization technique for thermal to cross domain colorized images,” arXiv preprint , 2021
2021
Closest in time.
R. Li, C. Wang, S. Liu, J. Wang, G. Liu, and B. Zeng, “Uphdr-gan: Generative adversarial network for high dynamic range imaging with unpaired data,” arXiv preprint , 2021
2021
Closest in time.
A. Jaiswal, A. R. Babu, M. Z. Zadeh, D. Banerjee, and F. Makedon, “A survey on contrastive self-supervised learning,” Technologies , vol. 9, no. 1, p. 2, 2021
2021
Closest in time.
L. Wang and K.-J. Yoon, “Psat-gan: Efficient adversarial attacks against holistic scene understanding,” IEEE Transactions on Image Processing , vol. 30, pp. 7541–7553, 2021
2021
Closest in time.
M. Alghamdi, Q. Fu, A. Thabet, and W. Heidrich, “Transfer deep learning for reconfigurable snapshot hdr imaging using coded masks,” in CGF . Wiley Online Library, 2021
2021
Closest in time.
E. Onzon, F. Mannan, and F. Heide, “Neural auto-exposure for high-dynamic range object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 7710–7720
2021
Closest in time.
L. Wang and K.-J. Yoon, “Semi-supervised student-teacher learning for single image super-resolution,” Pattern Recognition , vol. 121, p. 108206, 2022
2022
Closest in time.
Y. Zou, Y. Zheng, T. Takatani, and Y. Fu, “Learning to reconstruct high speed and high dynamic range videos from events,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 2024–2033
2033
Closest in time.