通過在深度學習中使用長短期損失來改善視頻運動一致性和無監督分割

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：59

、訪客IP：3.128.199.210

姓名

何迪亞(Wisnu Aditya) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

通過在深度學習中使用長短期損失來改善視頻運動一致性和無監督分割
(IMPROVING VIDEO MOTION COHERENCE AND UNSUPERVISED SEGMENTATION BY USING LONG- SHORT-TERM LOSS IN DEEP LEARNING)

相關論文

★ 基於edX線上討論板社交關係之分組機制	★ 利用Kinect建置3D視覺化之Facebook互動系統
★ 利用 Kinect建置智慧型教室之評量系統	★ 基於行動裝置應用之智慧型都會區路徑規劃機制
★ 基於分析關鍵動量相關性之動態紋理轉換	★ 基於保護影像中直線結構的細縫裁減系統
★ 建基於開放式網路社群學習環境之社群推薦機制	★ 英語作為外語的互動式情境學習環境之系統設計
★ 基於膚色保存之情感色彩轉換機制	★ 一個用於虛擬鍵盤之手勢識別框架
★ 分數冪次型灰色生成預測模型誤差分析暨電腦工具箱之研發	★ 使用慣性傳感器構建即時人體骨架動作
★ 基於多台攝影機即時三維建模	★ 基於互補度與社群網路分析於基因演算法之分組機制
★ 即時手部追蹤之虛擬樂器演奏系統	★ 基於類神經網路之即時虛擬樂器演奏系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

影像的分辨率越高，可以檢索到的信息越詳細。因此，分辨率小的影像會丟失很多重要的細節和資訊。儘管此資訊可用於各種工作，例如分類、檢測、追蹤或分割。我們已經看到使用深度學習技術（尤其是生成對抗網絡 (GAN)）來提高超分辨率影像質量的好的成果。然而，值得注意的是，在輸入和輸出具有明顯不同尺度的情況下進行超解析度處理將增加一定的難度。在本文中，我們提出了一種多步驟的超解析度方法，通過逐漸調整影像的尺度來實現最佳效果。影片超解析度（VSR）與單幀影像超解析度（SISR）存在不同的問題，因此需要採用不同的方法。大多數現有研究對於各幀之間的關系沒有的完全理解，但在VSR中，這一點至關重要，需要更多的關註。於此，時間特徵可以提供很多好處，在VSR中，它可以保持影片的品質和運動連續性，以及影片的一致性。通過這種方式，我們能夠避免影像中不一致性的錯誤隨時間累積的問題，而此問題是由於時間損失函數導致的。由我們的超分辨率方法的最終結果可以看出，它產生的超分辨率影片在使用此方法時，既保持影片質量又保持其運動連續性。此外，我們的結果在各種資料集中超越了SOTA方法。我們使用的其中一個資料集是煙花資料集。煙花有一個獨特的特點，在VSR中，它可以展現出清晰的動態，而在分割問題上，由於它不是固定單一的物體，而是會分離，具有動態的形狀和顏色，因此非常具有挑戰性。然而，還有其他需要解決的障礙，即缺乏具有煙花分割標簽的資料。因此，我們採用無監督學習的方法來自動進行分割。
關鍵詞：影片超分辨率，無監督分割，多跳，長期損失，影像處理，生成對抗網絡。

摘要(英)

The higher the resolution of an image, the more detailed information can be retrieved. Therefore, an image with a small resolution will lose a lot of important details and information. Even though this information can be used for various jobs such as classification, detection, tracking or segmentation. We have seen promising results from using deep learning techniques, especially Generative Adversarial Networks (GANs), to enhance the quality of super-resolution images. However, it is important to note that performing super resolutions with input and output that have a significantly different scale will add a certain amount of difficulty. Throughout this paper we propose the use of a super resolution that has multiple steps, which scales the image gradually in order to achieve maximum results. Video super resolution (VSR) has different problems with single image super resolution (SISR) so it requires a different approach. Most of the existing studies do not have a complete understanding of the relationship between frames, but in VSR this is crucial and needs more attention. There are numerous benefits that can be derived from the temporal feature, in VSR it can maintain video quality and motion continuity, as well as video consistency. In this way, we are able to avoid the inconsistent failures in the image which accumulate over time as a result of the temporal loss functions. The final result of our super resolution method can be seen in the fact that it produces a super-resolution video that maintains both the video′s quality as well as its motion continuity when using our proposed method. Moreover, our result is surpassing the state-of-the-art method in various datasets. One of the datasets that we use is Fireworks dataset. The fireworks have a unique characteristic, on VSR it can show the clear motion and on segmentation problem, it is very challenging due to the fireworks is not a solid object, also has a dynamic shape and color. However, there are other obstacles that need to be resolved, namely the unavailability of data that has segmentation labels for fireworks. Therefore, we take an unsupervised learning approach to be able to do segmentation automatically.
Keywords: Video super resolution, Unsupervised Segmentation, Multi-hop, long-term loss, Image Processing, Generative adversarial network.

關鍵字(中)

★ 影片超分辨率
★ 無監督分割
★ 多跳
★ 長期損失
★ 影像處理
★ 生成對抗網絡

關鍵字(英)

★ Video super resolution
★ Unsupervised Segmentation
★ Multi-hop
★ long-term loss
★ Image Processing
★ Generative adversarial network

論文目次

摘要 ii
ABSTRACT iv
ACKNOWLEDGESMENTS vi
TABLE OF CONTENT vii
LIST OF TABLES xii
ABBREVIATIONS xiii
CHAPTER 1 1
INTRODUCTION 1
1.1 Introduction 1
1.2 Objective of Research 5
1.3 Scope of Study 5
1.4 Limitation 6
1.5 Dissertation Outline 7
CHAPTER 2 8
LITERATURE REVIEW 8
2.1 Super Resolution 8
2.1.1 Single Image Super Resolution 8
2.1.2 Video Super Resolution 10
2.2 Generative Adversarial Network 12
2.2.1 Image Generation 12
2.2.2 Image-to-image Translation 13
2.2.3 Super Resolution 14
2.3 Image Segmentation 15
2.4 Optical Flow 16
CHAPTER 3 18
VIDEO SUPER RESOLUTION USING MULTI-HOP GAN WITH LONG TERM CONSISTENCY 18
3.1 Proposed Method 18
3.2 Multi-hop 20
3.3 Input 21
3.4 Generator 23
3.5 Discriminator 24
3.6 Loss Function 25
3.6.1 Short-term Loss 25
3.6.2 Long-term Loss 27
3.7 Data Preparation 29
3.7.1 Data Collection 29
3.7.2 Data Preprocessing 30
3.7.3 Data Augmentation 31
3.9 Summary 32
CHAPTER 4 34
UNSUPERVISED FIREWORKS VIDEO SEGMENTATION 34
4.1 Proposed Method 34
4.2 Segmentation Model 36
4.2.1 Input 36
4.3 Loss Function 37
4.4 Summary 38
CHAPTER 5 39
RESULT AND DISCUSSION 39
5.1 Video Super Resolution Result and Discussion 39
5.1.1 Implementation Detail 39
5.1.2 Evaluation Metrics 40
5.1.4 Experiment to Determine Generator Configuration 41
5.1.5 Experiments on Multi-hop Model 43
5.1.6 Experiment on Learning Rate 45
5.1.7 Experiment on Vimeo90K Dataset 46
5.1.8 Experiment on Vid4 Dataset 47
5.1.9 Experiment on Fireworks Dataset 50
5.2 Unsupervised Segmentation Result and Discussion 53
5.2.1 Implementation Detail 53
5.2.2 Evaluation Metrics 53
5.2.3 Experiments on Fireworks Unsupervised Segmentation 53
CHAPTER 6 57
CONCLUSION AND FUTURE WORKS 57
6.1 Conclusion 57
6.2 Future Works 58
References 59

參考文獻

[1] E. Koester and C.S. Sahin, A Comparison of Super-Resolution and Nearest Neighbors Interpolation Applied to Object Detection on Satellite Data, 2019, [Online]. Available: http://arxiv.org/abs/1907.05283.
[2] K. Nazeri, H. Thasarathan, and M. Ebrahimi, Edge-Informed Single Image Super-Resolution, in International Conference on Computer Vision Workshops (ICCV Workshops), 2019, pp. 3275– 3284, doi: 10.1109/ICCVW.2019.00409.
[3] B. Niu et al., Single Image Super-Resolution via a Holistic Attention Network, in European Conference on Computer Vision (ECCV ), Aug. 2020, pp. 191–207, doi: https://doi.org/10.1007/978-3-030-58610-2_12.
[4] S.J. Park, H. Son, S. Cho, K.S. Hong, and S.Lee, SRFeat: Single Image Super-Resolution with Feature Discrimination, in European Conference on Computer Vision, 2018, pp. 455–471, doi: https://doi.org/10.1007/978-3-030-01270-0_27.
[5] C. Dong, C.C. Loy, K. He, and X. Tang, Image Super-Resolution Using Deep Convolutional Networks, Dec.2014, [Online]. Available: http://arxiv.org/abs/1501.00092.
[6] C. Dong, C.C. Loy, and X. Tang, Accelerating the Super-Resolution Convolutional Neural Network, in European Conference on Computer Vision, 2016, pp. 391– 407, doi: 10.1007/978-3-319-46475-6.
[7] I. J. Goodfellow et al., Generative Adversarial Nets, Advances in Neural Information Processing Systems, Jun.2014.
[8] V. Sushko, J. Gall, and A. Khoreva, One-Shot GAN: Learning to Generate Samples from Single Images and Videos, in Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Mar. 2021, pp. 2596–2600, doi: 10.1109/CVPRW53098.2021.00293.
[9] P. Isola, J.Y. Zhu, T. Zhou, and A.A. Efros, Image-to-Image Translation with Conditional Adversarial Networks, in Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 5967–5976.
[10] Y. Lin, Y. Wang, Y. Li, Y. Gao, Z. Wang, and L. Khan, Attention-Based Spatial Guidance for Image-to-Image Translation, in Workshop on Applications of Computer Vision, 2021, pp. 816–825, doi: 10.1109/WACV48630.2021.00086.
[11] J. Kim, J. K. Lee, and K. M. Lee, Accurate Image Super-Resolution Using Very Deep Convolutional Networks, in Conference on Computer Vision and Pattern Recognition, Nov. 2016, pp. 1646–1654, [Online]. Available: http://arxiv.org/abs/1511.04587
[12] W. Shi et al., Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network, Sep.2016, [Online]. Available: http://arxiv.org/abs/1609.05158.
[13] C.Ledig et al., Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, in Conference on Computer Vision and Pattern Recognition, Sep. 2017, pp. 4681– 4690, doi: 10.1109/CVPR.2017.19.
[14] X. Wang et al., ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks, in European Conference on Computer Vision, 2018, pp. 63–79, doi: 10.1007/978-3-030-11021-5_5.
[15] M. S. M. Sajjadi, R. Vemulapalli, and M. Brown, Frame-Recurrent Video Super-Resolution, in Conference on Computer Vision and Pattern Recognition, Jan. 2018, pp. 6626–6634, doi: 10.1109/CVPR.2018.00693.
[16] X.Tao, H.Gao, R.Liao, J.Wang, and J.Jia, Detail-revealing Deep Video Super-resolution, in International Conference on Computer Vision (ICCV), 2017, pp. 4482–4490, doi: 10.1109/ICCV.2017.479.
[17] J. Long, E. Shelhamer, and T. Darrell, Fully Convolutional Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440. IEEE, Piscataway, NJ, 2015.
[18] O. Ronneberger, P. Fischer, and T. Brox, U-net: Convolutional networks for Biomedical Image Segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention(MICCAI), pp. 234–241, 2015.
[19] L.C. Chen, G. Papandreou, F. Schroff, and H. Adam, Rethinking Atrous Convolution for Semantic Image Segmentation. Preprint, arXiv:170605587, 2017.
[20] A. Ranjan et al., Competitive collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation, in Proceeding IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 12232–12241, 2019, doi: 10.1109/CVPR.2019.01252.
[21] S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz and D. Terzopoulos, Image Segmentation Using Deep Learning: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3523-3542, 1 July 2022, doi: 10.1109/TPAMI.2021.3059968.
[22] J.Gu, H.Lu, W.Zuo, and C.Dong, Blind Super-Resolution with Iterative Kernel Correction, in Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 11604–1613, doi: 10.1109/CVPR.2019.00170.
[23] A.Shocher, N.Cohen, and M.Irani, ‘Zero-Shot’ Super-Resolution using Deep Internal Learning, in Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 1043–1052, doi: 10.1109/CVPR.2018.00329.
[24] J.Johnson, A.Alahi, and L.Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, Mar. 2016, doi: 10.1007/978-3-319-46475-6_43.
[25] C.Ledig et al., Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, in Conference on Computer Vision and Pattern Recognition, Sep. 2017, pp. 4681– 4690, doi: 10.1109/CVPR.2017.19.
[26] X.Tao, H.Gao, R.Liao, J.Wang, and J.Jia, Detail-revealing Deep Video Super-resolution, in International Conference on Computer Vision (ICCV), 2017, pp. 4482–4490, doi: 10.1109/ICCV.2017.479
[27] D.Liu et al., Robust Video Super-Resolution with Learned Temporal Dynamics, in Proceedings of the IEEE International Conference on Computer Vision, Dec. 2017, vol. 2017-Octob, pp. 2526– 2534, doi: 10.1109/ICCV.2017.274.
[28] Y. J.Seoung, W.Oh, J.Kang, and S. J.Kim, Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation, in Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 3224–3232, doi: 10.1109/CVPR.2018.00340.
[29] Li, W., Tao, X., Guo, T., Qi, L., Lu, J., Jia, J. (2020). MuCAN: Multi-correspondence Aggregation Network for Video Super-Resolution. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. Computer Vision – ECCV 2020. ECCV 2020, vol 12355. Springer, Cham. doi: 10.1007/978-3-030-58607-2_20
[30] Isobe, T., Jia, X., Gu, S., Li, S., Wang, S., Tian, Q. (2020). Video Super-Resolution with Recurrent Structure-Detail Network. Computer Vision –ECCV 2020. Lecture Notes in Computer Science, vol 12357. Springer, Cham. doi: 10.1007/978-3-030-58610-2_38.
[31] J.Caballero et al., Real-time Video Super-Resolution with Spatio-temporal Networks and Motion Compensation, in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017, vol. 2017-Janua, pp. 2848–2857, doi: 10.1109/CVPR.2017.304.
[32] L.Wang, Y.Guo, L.Liu, Z.Lin, X.Deng, and W.An, Deep Video Super-Resolution Using HR Optical Flow Estimation, IEEE Transaction Image Processing, vol. 29, pp. 4323–4336, 2020, doi: 10.1109/TIP.2020.2967596.
[33] K. Chan, X. Wang, K. Yu, C. Dong and C. Loy, BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond, in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021 pp. 4945-4954.
[34] M.Chu, Y.Xie, J.Mayer, L.Leal-Taixé, and N.Thuerey, Learning Temporal Coherence via Self-supervision for GAN-based Video Generation, ACM Transaction on Graphics, vol. 39, no. 4, Jul.2020, doi: 10.1145/3386569.3392457.
[35] M.Haris, G.Shakhnarovich, and N.Ukita, Recurrent Back-Projection Network for Video Super-Resolution, in Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 13897–3906, doi: 10.1109/CVPR.2019.00402.
[36] A. Radford, L. Metz, and S. Chintala, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, In Proceeding 4th International Conference on Learning Representations ICLR 2016, pp. 1–16, 2016.
[37] T. Karras, T. Aila, S. Laine, and J. Lehtinen, Progressive growing of GANs for improved quality, stability, and variation, in Proceeding 6th International Conference on Learning Representations ICLR 2018, pp. 1–26, 2018.
[38] A. Brock, J. Donahue, and K. Simonyan, Large scale GAN training for high fidelity natural image synthesis, 7th International Conference Learn. Represent. ICLR 2019, pp. 1–35, 2019.
[39] J. Y. Zhu, T. Park, P. Isola and A. A. Efros, Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks, 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, pp. 2242-2251, doi: 10.1109/ICCV.2017.244.
[40] W. Xintao, X. Liangbin, D. Chao and S. Ying, Real-ESRGAN: Training Real-World Blind Super-Resolution With Pure Synthetic Data, In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 1905-1914.
[41] N. Otsu, A Threshold Selection Method from Gray-level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
[42] R. Nock and F. Nielsen, Statistical Region Merging, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1452–1458, 2004.
[43] N. Dhanachandra, K. Manglem, and Y. J. Chanu, Image Segmentation Using K-Means Clustering Algorithm and Subtractive Clustering Algorithm, Procedia Computer Science, vol. 54, pp. 764–771, 2015.
[44] L. Najman and M. Schmitt, Watershed of a Continuous Function, Signal Processing, vol. 38, no. 1, pp. 99–112, 1994.
[45] M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active Contour Models, International Journal of Computer Vision, vol. 1, no. 4, pp.321–331, 1988.
[46] Y. Boykov, O. Veksler, and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1222–1239, 2001.
[47] N. Plath, M. Toussaint, and S. Nakajima, Multi-class image segmentation using conditional random fields and global classification, in Proceedings of the 26th Annual International Conference on Machine Learning. ACM, 2009, pp. 817–824.
[48] J. L. Starck, M. Elad, and D. L. Donoho, Image Decomposition Via the Combination of Sparse Representations and a Variational Approach, IEEE transactions on image processing, vol. 14, no. 10, pp. 1570–1582, 2005.
[49] S. Minaee and Y. Wang, An ADMM Approach to Masked Signal Decomposition Using Subspace Representation, IEEE Transactions on Image Processing, vol. 28, no. 7, pp. 3192–3204, 2019.
[50] M. Chen, T. Artieres, and L. Denoyer, Unsupervised Object Segmentation by Redrawing. Advances in Neural Information Processing Systems. pp. 12705–12716. 2019.
[51] A. Bielski, and P. Favaro, Emergence of Object Segmentation in Perturbed Generative Models. In: Advances in Neural Information Processing Systems. pp. 7256–7266. 2019.
[52] T. Moriya, H. R. Roth, S. Nakamura, H. Oda, K. Nagara, M. Oda, and K. Mori , Unsupervised Pathology Image Segmentation Using Representation Learning with Spherical K-Means, in Proceeding Medical Imaging 2018: Digital Pathology. doi: 10.1117/12.2292172
[53] E. Ahn, A. Kumar, D. Feng, M. Fulham, and J. Kim. Unsupervised Feature Learning with K-means and An Ensemble of Deep Convolutional Neural Networks for Medical Image Classification. arXiv:1906.03359.
[54] Ji, X., Henriques, J.F., Vedaldi, A, Invariant Information Clustering for Unsupervised Image Classification and Segmentation. In Proceedings of the IEEE International Conference on Computer Vision. pp. 9865–9874, 2019.
[55] X. Xia and B. Kulis. W-net: A Deep Model for Fully Unsupervised Image Segmentation. arXiv:1711.08506, 2017.
[56] A. Kanezaki, Unsupervised Image Segmentation by Backpropagation. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1543–1547. IEEE, 2018.
[57] P. E. Srokosz, M. Bujko, M. Bocheńska, and R. Ossowski, Optical Flow Method for Measuring Deformation of Soil Specimen Subjected to Torsional Shearing, Measurement, Volume 174, 2021, 109064, doi: 0.1016/j.measurement.2021.109064.
[58] C.S. Royden and K.D. Moore, Use of Speed Cues in the Detection of Moving Objects by Moving Observers, Vision Research, Volume 59, 2012, Pages 17-24, doi: 10.1016/j.visres.2012.02.006.
[59] T.H. Kim, M.S.M. Sajjadi, M. Hirsch, and B. Schölkopf. Spatio-Temporal Transformer Network for Video Restoration. Computer Vision – ECCV 2018, vol 11207. Springer, Cham. doi: 10.1007/978-3-030-01219-9_7.
[60] Xue T, Chen B, Wu J, Wei D, Freeman WT (2019) Video Enhancement with Task-Oriented Flow. International Journal of Computer Vision (IJCV). Springer, 127:1106-1125.
[61] S.Akçay, A.Atapour-Abarghouei, and T. P.Breckon, Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection, Jan. 2019, doi: 10.1109/IJCNN.2019.8851808.
[62] X. Mao, Q. Li, H. Xie, R.Y.K. Lau, Z. Wang, and S.P. Smolley, Least Squares Generative Adversarial Networks. In: Proceedings of the IEEE International Conference on Computer Vision. IEEE, pp 2794-2802.
[63] J. Wang, G. Teng, and P.An, Video super-resolution based on generative adversarial network and edge enhancement, Electron., vol. 10, no. 4, pp. 1–19, Feb.2021, doi: 10.3390/electronics10040459.
[64] C. Yang, H. Lamdouar, E. Lu, A. Zisserman, W. Xie and P.An, Self-supervised Video Object Segmentation by Motion Grouping 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 2021, pp. 7157-7168, doi: 10.1109/ICCV48922.2021.00709.
[65] M. Hamilton, Z. Zhang, B. Hariharan, N. Snavely, and W. T. Freeman, Unsupervised Semantic Segmentation by Distilling Feature Correspondences, arXiv:2203.08414, 2022, doi:10.48550/arXiv.2203.08414.
[66] J. Johnson, A. Alahi, and L. Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution. In: European Conference on Computer Vision (ECCV). Springer International Publishing.
[67] X. Wang, K.C.K. Chan, K. Yu, C. Dong, C.C. Loy, EDVR: Video Restoration with Enhanced Deformable Convolutional Networks. In: Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, pp 1954-1963. doi: 10.1109/CVPRW.2019.00247.
[68] L. Wang, Y. Guo, Z. Lin, X. Deng, W. An, Learning for Video Super-Resolution Through HR Optical Flow Estimation. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science, vol 11361. Springer, Cham. https://doi.org/10.1007/978-3-030-20887-5_32.

指導教授

施國琛(Timothy K. Shih)

審核日期

2023-7-14

推文