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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 郭斯彥(Sy-Yen, Kuo) | |
| dc.contributor.author | Ke-Shuan Cheng | en |
| dc.contributor.author | 鄭克宣 | zh_TW |
| dc.date.accessioned | 2021-06-08T03:31:59Z | - |
| dc.date.copyright | 2019-08-13 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-12 | |
| dc.identifier.citation | [1] A. Baid, A. Kotwal, R. Bhalodia, S. Merchant, and S. P. Awate. Joint desmoking, specularity removal, and denoising of laparoscopy images via graphical models and bayesian inference. In Biomedical Imaging (ISBI 2017), 2017 IEEE 14th Interna- tional Symposium on, pages 732–736. IEEE, 2017.
[2] L. Breiman. Random forests. Machine learning, 45(1):5–32, 2001. [3] B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao. Dehazenet: An end-to-end system for single image haze removal. IEEE Transactions on Image Processing, 25(11):5187– 5198, 2016. [4] Z. Cai, Q. Fan, R. Feris, and N. Vasconcelos. A unified multi-scale deep convolu- tional neural network for fast object detection. In ECCV, 2016. [5] S. Calderara, P. Piccinini, and R. Cucchiara. Vision based smoke detection sys- tem using image energy and color information. Machine Vision and Applications, 22(4):705–719, 2011. [6] T. Çelik, H. Özkaramanlı, and H. Demirel. Fire and smoke detection without sen- sors: Image processing based approach. In 2007 15th European Signal Processing Conference, pages 1794–1798. IEEE, 2007. [7] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelli- gence, 40(4):834–848, 2017. [8] W.-T. Chen, S.-Y. Yuan, G.-C. Tsai, H.-C. Wang, and S.-Y. Kuo. Color channel- based smoke removal algorithm using machine learning for static images. In 2018 25th IEEE International Conference on Image Processing (ICIP), pages 2855–2859. IEEE, 2018. [9] A. Dakhia, T. Wang, and H. Lu. Multi-scale pyramid pooling network for salient object detection. Neurocomputing, 333:211–220, 2019. [10] R. Fattal. Single image dehazing. ACM transactions on graphics (TOG), 27(3):72, 2008. [11] R. Fattal. Dehazing using color-lines. ACM transactions on graphics (TOG), 34(1):13, 2014. [12] A.Filonenko, D.C.Hernández, and K.-H.Jo. Fast smoke detection for video surveil- lance using cuda. IEEE Transactions on Industrial Informatics, 14(2):725–733, 2018. [13] K. He, J. Sun, and X. Tang. Single image haze removal using dark channel prior. IEEE transactions on pattern analysis and machine intelligence, 33(12):2341–2353, 2011. [14] K. He, J. Sun, and X. Tang. Guided image filtering. IEEE transactions on pattern analysis & machine intelligence, (6):1397–1409, 2013. [15] K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence, 37(9):1904–1916, 2015. [16] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. [17] Y. Jia, J. Yuan, J. Wang, J. Fang, Q. Zhang, and Y. Zhang. A saliency-based method for early smoke detection in video sequences. Fire technology, 52(5):1271–1292, 2016. [18] D.P.Kingmaand, J.Ba.Adam: A method for stochastic optimization. arXivpreprint arXiv:1412.6980, 2014. [19] A. Kotwal, R. Bhalodia, and S. P. Awate. Joint desmoking and denoising of la- paroscopy images. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, pages 1050–1054. IEEE, 2016. [20] D.Krstinić, D.Stipaničev,and T.Jakovčević. Histogram-based smoke segmentation in forest fire detection system. Information Technology and Control, 38(3), 2009. [21] B. Li, X. Peng, Z. Wang, J. Xu, and D. Feng. Aod-net: All-in-one dehazing network. In Proceedings of the IEEE International Conference on Computer Vision, volume 1, page 7, 2017. [22] B.Li,W.Ren, D.Fu,D.Tao, D.Feng, W.Zeng, and Z.Wang. Benchmarking single-image dehazing and beyond. IEEE Transactions on Image Processing, 28(1):492– 505, 2018. [23] R. Li, J. Pan, Z. Li, and J. Tang. Single image dehazing via conditional generative adversarial network. methods, 3:24, 2018. [24] J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3431–3440, 2015. [25] G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan. Efficient image dehazing with boundary constraint and contextual regularization. In Proceedings of the IEEE in- ternational conference on computer vision, pages 617–624, 2013. [26] S. G. Narasimhan and S. K. Nayar. Chromatic framework for vision in bad weather. In Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on, volume 1, pages 598–605. IEEE, 2000. [27] W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M.-H. Yang. Single image dehazing via multi-scale convolutional neural networks. In European conference on computer vision, pages 154–169. Springer, 2016. [28] O.Ronneberger, P.Fischer, and T.Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015. [29] A. G. Sagar, S. Nath, and P. Nagrath. Smoke detection in digital frames. 2018. [30] N. Silberman, D. Hoiem, P. Kohli, and R. Fergus. Indoor segmentation and support inference from rgbd images. In European Conference on Computer Vision, pages 746–760. Springer, 2012. [31] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi. Inception-v4, inception-resnet and the impact of residual connections on learning. In Thirty-First AAAI Conference on Artificial Intelligence, 2017. [32] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Van- houcke, and A. Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9, 2015. [33] K. Tang, J. Yang, and J. Wang. Investigating haze-relevant features in a learning framework for image dehazing. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2995–3000, 2014. [34] J.-P. Tarel and N. Hautiere. Fast visibility restoration from a single color or gray level image. In Computer Vision, 2009 IEEE 12th International Conference on, pages 2201–2208. IEEE, 2009. [35] K. Tchaka, V. M. Pawar, and D. Stoyanov. Chromaticity based smoke removal in endoscopic images. In Medical Imaging 2017: Image Processing, volume 10133, page 101331M. International Society for Optics and Photonics, 2017. [36] H. Tian, W. Li, P. O. Ogunbona, and L. Wang. Detection and separation of smoke from single image frames. IEEE Transactions on Image Processing, 27(3):1164– 1177, 2018. [37] C. Wang, F. A. Cheikh, M. Kaaniche, and O. J. Elle. A smoke removal method for laparoscopic images. arXiv preprint arXiv:1803.08410, 2018. [38] C. Xiao and J. Gan. Fast image dehazing using guided joint bilateral filter. The Visual Computer, 28(6-8):713–721, 2012. [39] B.Xie, F.Guo, and Z.Cai. Improved single image dehazing using dark channel prior and multi-scale retinex. In Intelligent System Design and Engineering Application (ISDEA), 2010 International Conference on, volume 1, pages 848–851. IEEE, 2010. [40] T. Yamada and Y. Akizuki. Visibility and human behavior in fire smoke. In SFPE Handbook of Fire Protection Engineering, pages 2181–2206. Springer, 2016. [41] S. Yamaguchi, K. Hirai, and T. Horiuchi. Video smoke removal based on smoke imaging model and space-time pixel compensation. In International Workshop on Computational Color Imaging, pages 43–54. Springer, 2017. [42] D. Yang and J. SUN. Proximal dehaze-net: A prior learning-based deep network for single image dehazing. In Proceedings of the European Conference on Computer Vision (ECCV), pages 702–717, 2018. [43] J. Yang, F. Chen, and W. Zhang. Visual-based smoke detection using support vector machine. In Natural Computation, 2008. ICNC’08. Fourth International Conference on, volume 4, pages 301–305. IEEE, 2008. [44] J. Yu, C. Xiao, and D. Li. Physics-based fast single image fog removal. In Signal Processing (ICSP), 2010 IEEE 10th International Conference on, pages 1048–1052. IEEE, 2010. [45] S. Zagoruyko and N. Komodakis. Wide residual networks. arXiv preprint arXiv:1605.07146, 2016. [46] H. Zhang and V. M. Patel. Densely connected pyramid dehazing network. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. [47] H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia. Pyramid scene parsing network. In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages 2881–2890, 2017. [48] Y. Zhao. Candidate smoke region segmentation of fire video based on rough set theory. Journal of Electrical and Computer Engineering, 2015:11, 2015. [49] Q. Zhu, J. Mai, L. Shao, et al. A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Processing, 24(11):3522–3533, 2015. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21359 | - |
| dc.description.abstract | 煙霧常常會使得影像中能見度以及物件辨識準確度變低，而基於大氣模型進行除霧演的算法已經是一個被廣為研究的領域。但是，因為煙在影像上有著高度空間上的特性，例如煙在影像上的位置，以及，在鄰近的像素中比起霧有著更高範圍的濃度變化，使得除煙演算法並還沒有像除霧演算法一樣被廣泛的研究。也因上述的原因，直接使用除霧演算法為不切實際的做法，這些除霧演算法往往會造成影像色失真，尤其在無煙區域，進而使能見度及物件辨識的準確度並沒有有效提升。而為了解決這些問題，我們提出了一套基於卷積神經網路所組成的架構，這架構中包含了兩個子模型–煙區偵測網路(SRD-Net)和除煙網路(Desmoke-Net)來進行除煙。煙區偵測網路會先進行煙區的偵測，再者，使用除煙網路對所偵測的煙區來預估大氣模型的煙霧濃度圖，得到了煙霧濃度圖便能以大氣模型進行圖片能見度的修復。這套是第一個只需單張影像便能進行的除煙演算法，而且還保證了在無煙區域並不會有任何顏色上的改變，進而可以避免影像色失真，再者，其除煙的效果也比現行上的除霧演算法有著更為顯著的效果。 | zh_TW |
| dc.description.abstract | Smoke and haze usually deteriorate visibility of images and accuracy of object detection. Atmospheric model has been a popular approach to remove haze with single image. However, smoke removal, which possesses stronger spatial characteristic on image, has not been well studied yet due to difficulty of smoke positioning and high range intensity changes between neighbor pixels. Thus, it is impractical to directly apply existing haze removal algorithms on smoke removal. Moreover, color distortion is a common defect caused by haze removal algorithms. To solve problems mentioned above, a novel convolutional neural network architecture including two sub-models is proposed to recover smoky images: Smoke Region Detection Net (SRD-Net) is responsible for identifying smoky pixels. Desmoke-Net estimates transmission map, which is a key factor of atmospheric model, of smoky pixels. Images can be restored via atmospheric model and its estimated transmission map. The proposed architecture is the first smoke removal algorithm requiring single image only. Values of non-smoky region pixels also remain identical to avoid color distortion. Lastly, performance is better than existing haze removal algorithms on smoke removal. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T03:31:59Z (GMT). No. of bitstreams: 1 ntu-108-R06921083-1.pdf: 5989889 bytes, checksum: 6d9767511037bff3754ec79441c34e4f (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 口試委員會審定書 iii
誌謝 v 摘要 vii Abstract ix 1 Introduction 1 2 Related work 5 2.1 Atmospheric Model 5 2.2 Smoke Removal 6 2.3 Haze Removal 7 2.4 Smoky Region Detection Algorithms 8 3 Proposed Method 9 3.1 Color Channel-Based Smoke Removal 10 3.2 Invalid Smoke Removal 11 3.3 DesmokeNet 12 3.4 Detection Module 15 3.5 Atmospheric Light Estimation 16 3.6 Smoky Training Data Synthesis 17 4 Experimental Results 19 4.1 Implement Details 19 4.1.1 Training Process 19 4.1.2 Recovery Process 20 4.1.3 Dataset 20 4.2 Ablation study 21 4.2.1 Detection Module 21 4.2.2 Recovery Module 22 4.3 Comparison 22 4.3.1 Detection Module 23 4.3.2 Recovery Module 23 5 Conclusion 27 Bibliography 29 | |
| dc.language.iso | en | |
| dc.title | 使用卷積神經網路架構進行單張影像煙霧移除 | zh_TW |
| dc.title | Smoke Removal for Single Image based on Convolutional Neural Network Architecture | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 顏嗣鈞,雷欽隆,陳俊良,陳英一 | |
| dc.subject.keyword | 煙霧偵測,煙霧移除,卷機神經網路,深度學習,影像重建, | zh_TW |
| dc.subject.keyword | Smoke detection,Smoke Removal Convolutional Neural Network,Image Restoration, | en |
| dc.relation.page | 34 | |
| dc.identifier.doi | 10.6342/NTU201903030 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2019-08-12 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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| ntu-108-1.pdf 未授權公開取用 | 5.85 MB | Adobe PDF |
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