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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 李明穗 | |
dc.contributor.author | Nien-Hsin Chou | en |
dc.contributor.author | 周念新 | zh_TW |
dc.date.accessioned | 2021-05-13T06:49:02Z | - |
dc.date.available | 2020-09-01 | |
dc.date.available | 2021-05-13T06:49:02Z | - |
dc.date.copyright | 2017-08-25 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-08-18 | |
dc.identifier.citation | [1] A. Levin and Y. Weiss. User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior. IEEE Transactions on Pattern Analysis and Machine Intelligence(TPAMI), 29(9):1647–1654, 2007. [2] Y. Li and M. S. Brown. Single Image Layer Separation Using Relative Smoothness. In IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pages 2752–2759, 2014. [3] Y. Shih, D. Krishnan, F. Durand, and W. T. Freeman. Reflection Removal using Ghosting Cues. In IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pages 3193– 3201, 2015. [4] R. Wan, B. Shi, T. A. Hwee, and A. C. Kot. Depth of field guided reflection removal. In IEEE International Conference on Image Processing(ICIP), pages 21–25, 2016. [5] N. Arvanitopoulos, R. Achanta, and S. Süsstrunk. Single Image Reflection Suppression. In Computer Vision and Pattern Recognition (CVPR), 2017. [6] Y. Li and M. S. Brown. Exploiting Reflection Change for Automatic Reflection Removal. In IEEE International Conference on Computer Vision(ICCV), pages 2432–2439, 2013. [7] X. Guo, X. Cao, and Y. Ma. Robust Separation of Reflection from Multiple Images. In IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pages 2195–2202, 2014. [8] T. Xue, M. Rubinstein, C. Liu, and W. T. Freeman. A Computational Approach for Obstruction-free Photography. ACM Transactions on Graphics, 34(4):79:1–79:11, 2015. [9] C. Sun, S. Liu, T. Yang, B. Zeng, Z. Wang, and G. Liu. Automatic Reflection Removal Using Gradient Intensity and Motion Cues. In ACM Multimedia, MM ’16, pages 466–470, 2016. [10] J. Yang, H. Li, Y. Dai, and R. T. Tan. Robust Optical Flow Estimation of Double-Layer Images under Transparency or Reflection. In IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pages 1410-1419, 2016. [11] J. Lai, W. K. Leow, T. Sim, and V. Sharma. Think Big, Solve Small: Scaling Up Robust PCA with Coupled Dictionaries. In Applications of Computer Vision (WACV), pages 1-8, 2016. [12] N. Kong, Y. W. Tai, and J. S. Shin. A Physically-Based Approach to Reflection Separation: From Physical Modeling to Constrained Optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence(TPAMI), 36(2):209–221, 2014. [13] A. Agrawal, R. Raskar, S. K. Nayar, and Y. Li. Removing Photography Artifacts Using Gradient Projection and Flash-exposure Sampling. ACM Transactions on Graphics, 24(3):828–835, 2005. [14] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative Adversarial Nets. In NIPS, 2014. [15] P. Isola, J. Y. Zhu, T. Zhou, and A. A. Efros. Image-to-Image Translation with Conditional Adversarial Networks. arXiv preprint arXiv:1611.07004. 2016 [16] M. Arjovsky and L. Bottou. Towards Principled Methods for Training Generative Adversarial Networks. NIPS 2016 Workshop on Adversarial Training. In review for ICLR. Vol. 2016. 2017. [17] M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein GAN. arXiv preprint arXiv:1701.07875. 2017. [18] I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin,, and A. Courville. Improved Training of Wasserstein GANs. arXiv preprint arXiv:1704.00028. 2017. [19] I. Krasin, T. Duerig, N. Alldrin, A. Veit, S. Abu-El-Haija, S. Belongie, D. Cai, Z. Feng, V. Ferrari, V. Gomes, A. Gupta, D. Narayanan, C. Sun, G. Chechik, and K. Murphy. OpenImages: A public dataset for large-scale multi-label and multi-class image classification, 2016. Available from https://github.com/openimages. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/2739 | - |
dc.description.abstract | 去除影像中的反射現象一直是影像處理與電腦視覺中公認的難題之一。然而其所帶來的影響,輕則讓留有瞬間珍貴回憶的相片留下瑕疵,嚴重甚至會影響電腦視覺系統的運作,如監視系統等。因此一個可於合理時間與空間成本下運行之有效去反射的方法成為了許多研究者想要追求的目標。 在這本篇論文中,我們提出一個基於多目標生成對抗網路的學習架構來學習如何在合理的時間內盡可能地去除影像中的反射影像。透過修改原始生成對抗網路之損失函數與加入多目標學習架構,並配合資料生成模型產生出足夠支持學習架構之資料集使網路能學習到具母體代表性之參數組合。 相較於現有以最佳化為基礎的去反射演算法,我們的方法有兩大優勢:非常快的執行速度與超越現有方法之去反射結果。透過生成對抗網路的特性,我們成功地在去除反射的同時,保留了其他方法無法留下的材質與細節,大大提高了結果影像的品質。 | zh_TW |
dc.description.abstract | Removing reflection from the image is one of the hardest problem in the computer vision and image processing. However, its impact to our precious photos or computer vision system, like surveillance camera, is significant and desired to be solved. Therefore, we aimed to find out a practical approach to accomplish the removal in reasonable time and space. In this thesis, we proposed a new network architecture called Multi-Task Generative Adversarial Network. We trained network to learn how to remove reflection as much as possible and in proper time as well. In order to support learning, we proposed a data synthesis model that synthesize realistic reflection-containing images. By modifying loss function and imposing multi-task architecture into network, we expected our network can learning the parameters to separate reflection layer from background. Compared to major optimization-based algorithms, our network had two advantages: fast and effective. We successfully removed reflection and kept the texture and detail as well. By taking the advantage of Generative Adversarial Network, the quality of result has been significantly improved. | en |
dc.description.provenance | Made available in DSpace on 2021-05-13T06:49:02Z (GMT). No. of bitstreams: 1 ntu-106-R03944029-1.pdf: 2173257 bytes, checksum: e6882af9d32461f2c236d88109c88ab1 (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 口試委員會審定書 # 中文摘要 i ABSTRACT ii CONTENTS iii LIST OF FIGURES iv Chapter 1 Introduction 1 Chapter 2 Related Work 4 2.1 Multiple Images Reflection Removal 4 2.2 Single Image Reflection Removal 5 Chapter 3 Multi-Task Generative Adversarial Network on Single Image Reflection Removal 7 3.1 System Overview 7 3.2 Data Synthesis Model 10 3.3 Multi-Task Generative Adversarial Network 14 Chapter 4 Experimental Results 18 4.1 Experiment Settings 18 4.2 Qualitative Results 18 4.3 Compared with other single-image-based approaches 20 Chapter 5 Conclusion and Future Work 23 5.1 Conclusions 23 5.2 Future Work 23 REFERENCE 25 | |
dc.language.iso | en | |
dc.title | 利用多目標生成對抗網路去除單張影像反射現象 | zh_TW |
dc.title | Single Image Reflection Removal based on a Multi-Task Generative Adversarial Network | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 李界羲,周承復 | |
dc.subject.keyword | 影像去反射,生成對抗網路,多目標學習, | zh_TW |
dc.subject.keyword | Image Reflection Removal,Generative Adversarial Network,Multi-Task Learning, | en |
dc.relation.page | 27 | |
dc.identifier.doi | 10.6342/NTU201704038 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2017-08-19 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
顯示於系所單位: | 資訊網路與多媒體研究所 |
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ntu-106-1.pdf | 2.12 MB | Adobe PDF | 檢視/開啟 |
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