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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 歐陽明(Ming Ouhyoung) | |
| dc.contributor.author | Yi-Lung Kao | en |
| dc.contributor.author | 高以龍 | zh_TW |
| dc.date.accessioned | 2021-06-17T04:37:34Z | - |
| dc.date.available | 2023-07-30 | |
| dc.date.copyright | 2018-08-16 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-08-08 | |
| dc.identifier.citation | [1] F. Banterle, K. Debattista, A. Artusi, S. Pattanaik, K. Myszkowski, P. Ledda, and A. Chalmers. High dynamic range imaging and low dynamic range expansion for generating HDR content. Computer Graphics Forum, 28(8):2343–2367, 2009.
[2] F. Banterle, P. Ledda, K. Debattista, and A. Chalmers. Inverse tone mapping. In International conference on Computer graphics and interactive techniques in Australasia and Southeast Asia, 2006. [3] F.Banterle, P.Ledda, K.Debattista, and A.Chalmers. Expanding low dynamic range videos for high dynamic range applications. In Spring Conference on Computer Graphics, 2008. [4] F. Banterle, P. Ledda, K. Debattista, A. Chalmers, and M. Bloj. A framework for inverse tone mapping. The Visual Computer, 23(7):467–478, 2007. [5] C. Barnes, E. Shechtman, A. Finkelstein, and D. B. Goldman. PatchMatch: A ran-domized correspondence algorithm for structural image editing. ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH), 28(3):24, 2009. [6] Y.-C. Chang and J. F. Reid. RGB calibration for color image analysis in machine vision. IEEE Transactions on Image Processing, 5(10):1414–1422, 1996. [7] P. E. Debevec and J. Malik. Recovering high dynamic range radiance maps from photographs. ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH), 1997. [8] A. A. Efros and W. T. Freeman. Image quilting for texture synthesis and transfer. ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH), 2001. [9] G. Eilertsen, J. Kronander, G. Denes, R. K. Mantiuk, and J. Unger. HDR image reconstruction from a single exposure using deep CNNs. ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH Asia), 36(6):178:1–178:15, 2017. [10] Y.Endo, Y.Kanamori, and J.Mitani. Deep reverse tonemapping. ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH Asia), 36(6):177:1–177:10, 2017. [11] M. D. Fairchild. The HDR photographic survey. In Color and Imaging Conference, volume 2007, pages 233–238, 2007. [12] B. Funt and L. Shi. The effect of exposure on MaxRGB color constancy. In Human Vision and Electronic Imaging XV, volume 7527, page 75270Y, 2010. [13] B. Funt and L. Shi. The rehabilitation of MaxRGB. In Color and Imaging Confer- ence, volume 2010, pages 256–259, 2010. [14] M. D. Grossberg and S. K. Nayar. What is the space of camera response functions? In IEEE Conference on Computer Vision and Pattern Recognition, 2003. [15] S. Hajisharif, J. Kronander, and J. Unger. Adaptive DualISO HDR reconstruction. EURASIP Journal on Image and Video Processing, 2015(1):41, 2015. [16] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition, 2016. [17] J.-B.Huang, S.B.Kang, N.Ahuja, and J.Kopf. Image completion using planar structure guidance. ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH), 33(4):129, 2014. [18] S. Iizuka, E. Simo-Serra, and H. Ishikawa. Globally and locally consistent image completion. ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH), 36(4):107, 2017. [19] N. K. Kalantari and R.Ramamoorthi. Deep high dynamic range imaging of dynamic scenes. ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH Asia), 36(4):144:1–144:12, 2017. [20] E. A. Khan, A. O. Akyuz, and E. Reinhard. Ghost removal in high dynamic range images. In IEEE International Conference on Image Processing, 2006. [21] D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. In Interna- tional Conference on Learning Representations, 2014. [22] J. Kronander, S. Gustavson, G. Bonnet, and J. Unger. Unified HDR reconstruction from raw CFA data. In IEEE International Conference on Computational Photography, 2013. [23] J. Kronander, S. Gustavson, G. Bonnet, A. Ynnerman, and J. Unger. A unified framework for multi-sensor HDR video reconstruction. Signal Processing: Image Communication, 29(2):203–215, 2014. [24] H. Li and P. Peers. Crf-net: Single image radiometric calibration using CNNs. In European Conference on Visual Media Production, 2017. [25] S. Lin, J. Gu, S. Yamazaki, and H.-Y. Shum. Radiometric calibration from a single image. In IEEE Conference on Computer Vision and Pattern Recognition, 2004. [26] S. Lin and L. Zhang. Determining the radiometric response function from a single grayscale image. In IEEE Conference on Computer Vision and Pattern Recognition, 2005. [27] S. Mangiat and J. Gibson. High dynamic range video with ghost removal. In Appli- cations of Digital Image Processing XXXIII, 2010. [28] S. Mann and R. W. Picard. On being ‘undigital’ with digital cameras: Extending dynamic range by combining differently exposed pictures. In Proceedings of IS&T, 1995. [29] R. Mantiuk, K. J. Kim, A. G. Rempel, and W. Heidrich. HDR-VDP-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions. In ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH), volume 30, page 40, 2011. [30] D. Marnerides, T. Bashford-Rogers, J. Hatchett, and K. Debattista. ExpandNet: A deep convolutional neural network for high dynamic range expansion from low dy- namic range content. In Eurographics, 2018. [31] Y. Matsushita and S. Lin. Radiometric calibration from noise distributions. In IEEE Conference on Computer Vision and Pattern Recognition, 2007. [32] T. Mitsunaga and S. K. Nayar. Radiometric self calibration. In IEEE Conference on Computer Vision and Pattern Recognition, 1999. [33] S. K. Nayar and T. Mitsunaga. High dynamic range imaging: Spatially varying pixel exposures. In IEEE Conference on Computer Vision and Pattern Recognition, 2000. [34] T.-T. Ng, S.-F. Chang, and M.-P. Tsui. Using geometry invariants for camera response function estimation. In IEEE Conference on Computer Vision and Pattern Recognition, 2007. [35] D.Pathak,P.Krahenbuhl,J.Donahue,T.Darrell,andA.A.Efros.Contextencoders: Feature learning by inpainting. In IEEE Conference on Computer Vision and Pattern Recognition, 2016. [36] E. Reinhard, W. Heidrich, P. Debevec, S. Pattanaik, G. Ward, and K. Myszkowski. High Dynamic Range Imaging: Acquisition, Display, and Image-based Lighting. 2010. [37] 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, 2015. [38] M. Rouf, R. Mantiuk, W. Heidrich, M. Trentacoste, and C. Lau. Glare encoding of high dynamic range images. In IEEE Conference on Computer Vision and Pattern Recognition, 2011. [39] A.Serrano, F.Heide, D.Gutierrez, G.Wetzstein, and B.Masia. Convolutional sparse coding for high dynamic range imaging. Computer Graphics Forum, 35(2):153–163, 2016. [40] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations, 2015. [41] A. Srikantha and D. Sidibé. Ghost detection and removal for high dynamic range images: Recent advances. Signal Processing: Image Communication, 27(6):650– 662, 2012. [42] N.-S. Syu, Y.-S. Chen, and Y.-Y. Chuang. Learning deep convolutional networks for demosaicing. arXiv:1802.03769, 2018. [43] J. Takamatsu, Y. Matsushita, and K. Ikeuchi. Estimating radiometric response func- tions from image noise variance. In European Conference on Computer Vision, 2008. [44] M. D. Tocci, C. Kiser, N. Tocci, and P. Sen. A versatile HDR video production sys- tem. ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH), 30(4):41, 2011. [45] G. Ward et al. High dynamic range image encodings. 2006. [46] F. Xiao, J. M. DiCarlo, P. B. Catrysse, and B. A. Wandell. High dynamic range imaging of natural scenes. In Color and Imaging Conference, volume 2002, pages 337–342, 2002. [47] J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. S. Huang. Generative image inpainting with contextual attention. In IEEE Conference on Computer Vision and Pattern Recognition, 2018. [48] J. Zhang and J.-F. Lalonde. Learning high dynamic range from outdoor panoramas. In IEEE International Conference on Computer Vision, 2017. [49] H. Zhao, B. Shi, C. Fernandez-Cull, S.-K. Yeung, and R. Raskar. Unbounded high dynamic range photography using a modulo camera. In IEEE International Conference on Computational Photography, 2015. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70762 | - |
| dc.description.abstract | 我們提出了一個基於深度學習的方式，從單張低動態範圍影 像 (LDR) 重建高動態範圍影像 (HDR) 。這個問題非常具有挑戰性， 因為目前相機結構上的限制，在拍攝照片時會有影像量化的問題 (quantization) 以及過曝的問題。與前人所提出的方法不同點是:我們提出的網路架構並不直接學習低動態範圍影像與高動態範圍影像之間的對應關係，我們在網路架構中加入了更多人類關於相機與影像的知識。明確的說，我們提出的網路架構含有三個部分:(1) 影像線性化 (2) 影像去量化 (3) 影像去過曝。第一，給訂一張低動態範圍影像，我們先找出對應的相機反應函數，便可以將影像轉換到線性的空間中。 第二，我們透過影像去量化網路來去除影像過暗區域所造成的影像量化問題。第三，我們使用影像去過曝網路來重新繪畫出因為過曝所缺失的影像內容。最後，我們對所提出的演算法與同類型演算法做了許多實驗與比較，證明我們的演算法優於前人所提出的演算法。 | zh_TW |
| dc.description.abstract | We present a learning-based approach for recovering a high dynamic range (HDR) image from a single low dynamic range (LDR) input image. This problem is challenging due to missing details in under-/over-exposed regions caused by quantization and saturation of camera sensors. In contrast to ex- isting methods that directly learn the mapping from LDR to HDR images using a generic network, we propose to integrate the domain knowledge of the LDR image formation pipeline into our model. Specifically, our ap- proach consists of three modules to restore HDR details from LDR images: (1) linearization, (2) dequantization, and (3) hallucination. First, given an input LDR image, we estimate the inverse camera response function using a Linearization-Net and map the LDR image to the linear space. Second, we apply a Dequantization-Net to remove quantization artifacts in the under- exposed regions. Third, we inpaint the missing regions due to saturation with a Hallucination-Net. Extensive quantitative and qualitative experiments demonstrate that our approach performs favorably against state-of-the-art tech- niques by learning to reverse the image formation pipeline. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T04:37:34Z (GMT). No. of bitstreams: 1 ntu-107-R05922122-1.pdf: 64663586 bytes, checksum: 25fa65dc27880d5c4699460c595aeaf0 (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 誌謝 ii
Acknowledgements iii 摘要 iv Abstract v 1 Introduction 1 2 Related work 4 2.1 Hardware-basedHDRreconstruction 4 2.2 Multi-imageHDRreconstruction 4 2.3 Single-imageHDRreconstruction 5 2.4 Radiometric calibration 5 2.5 Imagecompletion 6 3 Learning to Reverse the Camera Pipeline 7 3.1 LDRimageformation 7 3.2 Linearization 10 3.3 Dequantization 12 3.4 Hallucination 12 4 Experimental Results 14 4.1 Experimentalsettings 14 4.2 Implementationdetails 15 4.3 Comparisonswiththestate-of-the-artmethods 16 4.4 Contribution of individual components 17 4.5 Limitations 18 5 Conclusions 20 6 Result Images 21 | |
| dc.language.iso | zh-TW | |
| dc.subject | 圖像處理 | zh_TW |
| dc.subject | 反向色調映射 | zh_TW |
| dc.subject | 高動態範圍影像 | zh_TW |
| dc.subject | 卷積神經網絡 | zh_TW |
| dc.subject | 輻射校準 | zh_TW |
| dc.subject | Radiometric Calibration | en |
| dc.subject | Image Processing | en |
| dc.subject | Convolutional Neural Networks | en |
| dc.subject | High Dynamic Range Imaging | en |
| dc.subject | Reverse Tone Mapping | en |
| dc.title | 使用深度學習從單張低動態範圍影像重建高動態範圍影像 | zh_TW |
| dc.title | Using Convolutional Neural Network to Reconstruct High Dynamic Range Image from a Single Low Dynamic Range Image | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 梁容輝(Rung-Huei Liang),傅楸善(Chiou-Shann Fuh) | |
| dc.subject.keyword | 反向色調映射,高動態範圍影像,卷積神經網絡,輻射校準,圖像處理, | zh_TW |
| dc.subject.keyword | Reverse Tone Mapping,High Dynamic Range Imaging,Convolutional Neural Networks,Radiometric Calibration,Image Processing, | en |
| dc.relation.page | 51 | |
| dc.identifier.doi | 10.6342/NTU201801416 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-08-08 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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