Acknowledgement
This work is supported by a grant (22TSRD-C151228-04) from Urban Declining Area Regenerative Capacity-Enhancing Technology Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government
References
- F. J. Romero-Ramirez, R. Muoz-Salinas, and R. MedinaCarnicer, "Speeded up detection of squared fiducial markers," Image and Vision Computing, vol. 76, August, 2018, DOI: 10.1016/j.imavis.2018.05.004.
- E. Ilg, N. Mayer, T, Saikia, M. Keuper, A. Dosovitskiy, and T. Brox, "Flownet 2.0: Evolution of optical flow estimation with deep networks," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, DOI: 10.1109/CVPR.2017.179.
- R. Dulski, P. Powalisz, M. Kastek, and P. Trzaskawka, "Enhancing image quality produced by IR cameras," Electro-Optical and Infrared Systems: Technology and Applications VII, 2010, DOI: 10.1117/12.864979.
- V. T. Tran, B.-S. Yang, F. Gu, and A. Ball, "Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis," Mechanical Systems and Signal Processing, vol. 38, no. 2, pp. 601-614, July, 2013, DOI: 10.1016/j.ymssp.2013.02.001.
- S. Agaian and M. Roopaei, "Novel infrared and thermal image enhancement algorithms," Mobile Multimedia/Image Processing, Security, and Applications 2013, 2013, DOI: 10.1117/12.2016040.
- Y. Choi, N. Kim, S. Hwang, and I. S. Kweon, "Thermal image enhancement using convolutional neural network," 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, South Korea, 2016, DOI: 10.1109/IROS.2016.7759059.
- K. Lee, J. Lee, J. Lee, S. Hwang, and S. Lee, "Brightness based convolutional neural network for thermal image enhancement," IEEE Access, vol. 5, 2017, DOI: 10.1109/ACCESS.2017.2769687.
- S. Shah and J . K. Aggarwal, "Depth estimation u sing s tereo fish-eye lenses," 1st International Conference on Image Processing, Austin, TX, USA, 1994, DOI: 10.1109/ICIP.1994.413669.
- A. N. Rajagopalan, S. Chaudhuri, and Uma Mudenagudi, "Depth estimation and image restoration using defocused stereo pairs," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 11, Nov., 2004, DOI: 10.1109/TPAMI.2004.102.
- Y. Liu, X. Cao, Q. Dai, and W. Xu, "Continuous depth estimation for multi-view stereo," 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 2009, DOI: 10.1109/CVPR.2009.5206712.
- D. Eigen, C. Puhrsch, and R. Fergus, "Depth map prediction from a single image using a multi-scale deep network," arXiv: 1406.2283, 2014, DOI: 10.48550/arXiv.1406.2283.
- I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab, "Deeper depth prediction with fully convolutional residual networks," 2016 Fourth international conference on 3D vision (3DV), Stanford, CA, USA, 2016, DOI: 10.1109/3DV.2016.32.
- R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V. Koltun, "Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 33, March, 2020, DOI: 10.1109/TPAMI.2020.3019967.
- N. Kim, Y. Choi, S. Hwang, and I. S. Kweon, "Multispectral Transfer Network: Unsupervised Depth Estimation for All-Day Vision," Thirty-Second AAAI Conference on Artificial Intelligence, vol. 32, no. 1, 2018, [Online], https://ojs.aaai.org/index.php/AAAI/article/view/12297.
- Y. Lu and G. Lu, "An alternative of lidar in nighttime: Unsupervised depth estimation based on single thermal image," 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2021, DOI: 10.1109/WACV48630.2021.00388.
- Z. Zhang, "A flexible new technique for camera calibration," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, Nov., 2000, DOI: 10.1109/34.888718.
- T. Zhou, M. Brown, N. Snavely, and D. G. Lowe, "Unsupervised learning of depth and ego-motion from video," arXiv:1704.07813, 2017, DOI: 10.48550/arXiv.1704.07813.
- R. Garg, B. G. V. Kumar, G. Carneiro, and I. Reid, "Unsupervised cnn for single view depth estimation: Geometry to the rescue," European Conference on Computer Vision, 2016, DOI: 10.1007/978-3-319-46484-8_45.
- C. Godard, O. M. Aodha, M. Firman, and G. Brostow, "Digging into self-supervised monocular depth estimation," 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019, DOI: 10.1109/ICCV.2019.00393.
- C. Godard, O. M. Aodha, and G. J. Brostow, "Unsupervised monocular depth estimation with left-right consistency," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, DOI: 10.1109/CVPR.2017.699.
- Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE Transactions on Image Processing, vol. 13, no. 4, Apr., 2004, DOI: 10.1109/TIP.2003.819861.
- Z. Li and N. Snavely, "Megadepth: Learning single-view depth prediction from internet photos," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, DOI: 10.1109/CVPR.2018.00218.
- J. H. Lee, M.-K. Han, D. W. Ko, and I. Suh, "From big to small: Multi-scale local planar guidance for monocular depth estimation," arXiv:1907.10326, 2019, DOI: 10.48550/arXiv,1907.10326.