El-Alfy, H., Mitsugami, I., Yagi, Y.: Gait recognition based on normal distance maps. IEEE Trans. Cybern. 48, 1526–1539 (2018)
Li, J., Pei, Z., Zeng, T.: From beginner to master: a survey for deep learning-based single-image super-resolution. arXiv. pp. 1–20 (2021)
Takeshima, H., Kaneko, T.: Image registration using subpixel-shifted images for super-resolution. In: IEEE International Conference on Image Processing, pp. 2404–2407. IEEE (2008)
Baker, S., Kanade, T.: Hallucinating faces. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 83–88. IEEE (2000)
Liu, C., Shum, H.-Y., Freeman, W.T.: Face hallucination: theory and practice. Int. J. Comput. Vis. 75, 115–134 (2007)
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: European Conference on Computer Vision, pp. 184–199. Springer (2014)
Donoho, D.: Compressed sensing. IEEE Trans. Inform. Theory 52, 1289–1306 (2006)
Article MathSciNet MATH Google Scholar
Sasao, T., Hiura, S., Sato, K.: Super-resolution with randomly shaped pixels and sparse regularization. In: IEEE International Conference on Computational Photography, pp. 1–11. IEEE (2013)
Portnoy, A.D., Pitsianis, N.P., Brady, D.J., Guo, J., Fiddy, M.A., Feldman, M.R., Te Kolste, R.D.: Thin digital imaging systems using focal plane coding. Proc. SPIE 6065, 60650F (2006)
Slinger, C., Bennett, H., Dyer, G., Gordon, N., Huckridge, D., McNie, M., Penney, R., Proudler, I., Rice, K., Ridley, K., Russell, L., de Villiers, G., Watson, P.: Adaptive coded-aperture imaging with subpixel superresolution. Opt. Lett. 37, 854–856 (2012)
Yoshida, M., Torii, A., Okutomi, M., Endo, K., Sugiyama, Y., Taniguchi, R., Nagahara, H.: Joint optimization for compressive video sensing and reconstruction under hardware constraints. In: European Conference on Computer Vision, pp. 634–649. Springer (2018)
Sitzmann, V., Diamond, S., Peng, Y., Dun, X., Boyd, S., Heidrich, W., Heide, F., Wetzstein, G.: End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging. ACM Trans. Graph. 37, 114 (2018)
Wang, C., Chen, N., Heidrich, W.: dO: a differentiable engine for deep lens design of computational imaging systems. IEEE Trans. Comput. Imaging 8, 905–916 (2022)
Kumawat, S., Okawara, T., Yoshida, M., Nagahara, H., Yagi, Y.: Action recognition from a single coded image. IEEE Trans. Pattern Anal. Mach. Intell. 14, 1–14 (2022)
Fofi, D., Sliwa, T., Voisin, Y.: A comparative survey on invisible structured light. In: Machine Vision Applications in Industrial Inspection XII, vol. 5303. pp. 90–98. SPIE (2004)
Mori, A., Makihara, Y., Yagi, Y.: Gait recognition using period-based phase synchronization for low frame-rate videos. In: International Conference on Pattern Recognition, pp. 2194–2197. IEEE (2010)
Chaaraoui, A.A., Padilla-López, J.R., Flórez-Revuelta, F.: Fusion of skeletal and silhouette-based features for human action recognition with RGB-D devices. In: International Conference on Computer Vision Workshops, pp. 91–97. IEEE (2013)
Mottaghi, A., Soryani, M., Seifi, H.: Action recognition in freestyle wrestling using silhouette-skeleton features. Eng. Sci. Technol. Int J. 23, 921–930 (2020)
Huynh, O., Stanciulescu, B.: Person re-identification using the silhouette shape described by a point distribution model. In: Winter Conference on Applications of Computer Vision, pp. 929–934. IEEE (2015)
Liao, R., Makihara, Y., Muramatsu, D., Mitsugami, I., Yagi, Y., Yoshiyama, K., Kazui, H., Takeda, M.: A video-based gait disturbance assessment tool for diagnosing idiopathic normal pressure hydrocephalus. IEEJ Trans. Electr. Electron. Eng. 15, 433–441 (2020)
Sakoda, S., Nakamura, T., Yagi, Y.: End-to-End Optimization Approach for Super-Resolution Imaging of Human Silhouettes through Coded Illumination and Reconstruction Network. In: Frontiers in Optics + Laser Science 2024 (FiO, LS). p. JW5A.68. Optica Publishing Group (2024)
Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning low-level vision. Int. J. Comput. Vis. 40, 25–47 (2000)
Freeman, W., Jones, T., Pasztor, E.: Example-based super-resolution. IEEE Comput. Graph. 22, 56–65 (2002)
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., Shi, W.: Photo-realistic single image super-resolution using a generative adversarial network. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2017)
He, B., Wang, G., Lin, X., Shi, C., Liu, C.: High-accuracy sub-pixel registration for noisy images based on phase correlation. IEICE Trans. Inf. Syst. E94.D, 2541–2544 (2011)
Article ADS MATH Google Scholar
Nishi, K.: 3 coded exposure and super-resolution. J. ITE 67, 655–660 (2013)
Ashok, A., Neifeld, M.A.: Pseudorandom phase masks for superresolution imaging from subpixel shifting. Appl. Opt. 46, 2256–2268 (2007)
Kawachi, H., Nakamura, T., Iwata, K., Makihara, Y., Yagi, Y.: Snapshot super-resolution indirect time-of-flight camera using a grating-based subpixel encoder and depth-regularizing compressive reconstruction. Opt. Contin. 2, 1368–1383 (2023)
Li, F., Chen, H., Pediredla, A., Yeh, C., He, K., Veeraraghavan, A., Cossairt, O.: CS-ToF: high-resolution compressive time-of-flight imaging. Opt. Express 25, 31096–31110 (2017)
Katano, Y., Nobukawa, T., Muroi, T., Hagiwara, K., Ishii, N.: Real-time super-resolution imaging using coded aperture. In: Emerging Digital Micromirror Device Based Systems and Applications XIV, p. PC1201404. SPIE (2022)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2016)
Lei, S., Shi, Z., Wu, X., Pan, B., Xu, X., Hao, H.: Simultaneous super-resolution and segmentation for remote sensing images. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 3121–3124. IEEE (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer (2015)
Jadon, S.: A survey of loss functions for semantic segmentation. In: IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, pp. 1–7. IEEE (2020)
Jacome, R., Gomez, P., Arguello, H.: Middle output regularized end-to-end optimization for computational imaging. Optica 10, 1421–1431 (2023)
Goodfellow, I.J., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Takemura, N., Makihara, Y., Muramatsu, D., Echigo, T., Yagi, Y.: Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Trans. Comput. Vis. Appl. 10, 1–14 (2018)
Qin, X., Zhang, Z., Huang, C., Dehghan, M., Zaiane, O.R., Jagersand, M.: U2-Net: going deeper with nested u-structure for salient object detection. Pattern Recognit. 106, 107404 (2020)
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, vol. 2022-June, pp. 10674–10685. IEEE (2022)
Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: IEEE/CVF International Conference on Computer Vision, pp. 3836–3847. IEEE (2023)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
留言 (0)