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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 電子工程學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66326
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dc.contributor.advisor簡韶逸(Shao-Yi Chien)
dc.contributor.authorYi-Chun Linen
dc.contributor.author林奕君zh_TW
dc.date.accessioned2021-06-17T00:30:27Z-
dc.date.available2017-03-19
dc.date.copyright2012-03-19
dc.date.issued2012
dc.date.submitted2012-02-12
dc.identifier.citation[1] A. Heinrich, G. de Haan, and C.N. Cordes, “A novel performance measure for picture rate conversion methods,” in Digest of Technical Papers of International Conference on Consumer Electronics, Jan. 2008, pp. 1–2.
[2] Q.Wang and R.K.Ward, “A new orientation-adaptive interpolation method,”IEEE Transactions on Image Processing, vol. 16, no. 4, pp. 889–900, April 2007.
[3] R. Fattal, “Image upsampling via imposed edge statistics,” in SIGGRAPH’07: ACM SIGGRAPH 2007 papers, 2007, p. 95.
[4] D. Glasner, S. Bagon, and M. Irani, “Super-resolution from a single image,”in 2009 IEEE 12th International Conference onComputer Vision,, 2009, pp.349 –356.
[5] S. Farsiu, M.D. Robinson, M. Elad, and P. Milanfar, “Fast and robust multiframe super resolution,” IEEE Transactions on Image Processing, vol. 13, no. 10, pp. 1327–1344, Oct. 2004.
[6] M. Protter, M. Elad, H. Takeda, and P.Milanfar, “Generalizing the nonlocal means to super-resolution reconstruction,” IEEE Transactions on Image Processing,
vol. 18, no. 1, pp. 36–51, Jan. 2009.
[7] B. Bascle, A. Blake, and A. Zisserman, “Motion deblurring and superresolution from an image sequence,” in In Proceedings of the Fourth European Conference on Computer Vision. 1996, pp. 573–582, Springer-Verlag.
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[10] T. Blu and M. Unser, “Image interpolation and resampling,” in Handbook of Medical Imaging, Processing and Analysis. 2000, pp. 393–420, Academic Press.
[11] X. Li and M.T. Orchard, “New edge-directed interpolation,” IEEE Transactions on Image Processing, vol. 10, no. 10, pp. 1521–1527, Oct 2001.
[12] D Su and P Willis, “Image interpolation by pixel-level data-dependent triangulation,”Computer Graphics Forum, vol. 23, no. 2, pp. 189–201, 2004.
[13] Q. Shan, Z. Li, J. Jia, and C.-K. Tang, “Fast image/video upsampling,” ACM Transactions on Graphics (SIGGRAPH ASIA), vol. 27, no. 5, pp. 153, 2008.
[14] J. Sun, Z. Xu, and H.-Y. Shum, “Image super-resolution using gradient profile prior,” in 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008, pp. 1 –8.
[15] Jian Sun, Nan ning Zheng, Hai Tao, and Heung yeung Shum, “Image hallucination with primal sketch priors,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR, 2003, pp. 729–736.
[16] Hussein A Aly and Eric Dubois, “Image up-sampling using total-variation regularization with a new observation model.,” IEEE Transactions on Image Processing, vol. 14, no. 10, pp. 1647–1659, 2005.
[17] S. Baker and T. Kanade, “Limits on super-resolution and how to break them,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 9, pp. 1167–1183, Sep 2002.
[18] S. Baker and T. Kanade, “Hallucinating faces,” in FOURTH INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION, 1999.
[19] W.T. Freeman, T.R. Jones, and E.C. Pasztor, “Example-based superresolution,”IEEE Computer Graphics and Applications, vol. 22, no. 2, pp.56–65, Mar/Apr 2002.
[20] H. Chang, D.-Y. Yeung, and Y. Xiong, “Super-resolution through neighbor embedding,” in In Proc. CVPR 2004, vol. 1, pp. I–275 – I–282.
[21] K.I. Kim and Y. Kwon, “Single-image super-resolution using sparse regression and natural image prior,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 32, no. 6, pp. 1127 –1133, june 2010.
[22] M. Planck, K. I. Kim, and Y. Kwon, “Example-based learning for singleimage super-resolution and jpeg artifact removal,” Biological Cybernetics, , no. August, 2008.
[23] Y.-W. Tai, S. Liu, M.S. Brown, and S. Lin, “Super resolution using edge prior and single image detail synthesis,” in 2010 IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), 2010, pp. 2400 –2407.
[24] J. Sun, J. Zhu, and M.F. Tappen, “Context-constrained hallucination for image super-resolution,” in 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010, pp. 231 –238.
[25] Y. HaCohen, R. Fattal, and D. Lischinski, “Image upsampling via texture hallucination,” in 2010 IEEE International Conference on Computational Photography (ICCP),, 2010, pp. 1 –8.
[26] Y.-N. Liu, Y.-C. Lin, and S.-Y. Chien, “A no-reference quality evaluation method for cfa demosaicking,” in Digest of Technical Papers of International Conference on Consumer Electronics, Jan. 2010, pp. 365–366.
[27] M. F. Barnsley, “Fractals everywhere,” in Academic Press, New York,1988.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66326-
dc.description.abstractThe goal of super resolution is to recover the high-resolution image with sharp edges and rich details from a low-resolution input image. Due to the increasing gap between the resolution of image sources and display devices, super resolution has become an essential technique in many applications. In this work, we focus on the application of TV scaler. Due to real-time requirement and low hardware-cost constraints, conventional TV scaler can only employ basic interpolation technique thus introduces some artifacts that degrade the viewing quality of the output sequences. Therefore, the goal of this work is to improve the performance of TV scaler by adopting the super resolution technique. The corresponded hardware design is also provided.
We propose a low complexity super resolution algorithm which can provide vivid output image with rich details and sharp edges. There are two main contributions. First is the development of double interpolation up-sampling. Double interpolation quality evaluation can be used as a measurement of an interpolation operation. By using this double interpolation framework, the direction-adaptive upsampling algorithm is proposed to solve the zigzag artifact and enhance the quality of edges. The second contribution is the database-free texture synthesis technique. Based on the fractal property of nature images, it is possible to find proper high resolution patches in low resolution input image itself. Therefore, the texture synthesis can be performed without database to provide proper and rich details. The double interpolation framework for up-sampling and the reconstruction constraint for the final optimization combined with the texture synthesis form the whole super resolution algorithm. Experimental results show that the proposed super resolution algorithm performs better than other ones.
For the VLSI hardware design, the target specification is set to 1920x1080 frame size, with throughput of 60 frames per second. The main contributions of hardware architecture design are one-pass double interpolation, tile-based gradient descent, and partial-sum reuse texture synthesis. One-pass double interpolation and tile-based gradient descent lower down the consumption of bandwidth and SRAM, while partial-sum reuse texture synthesis reduce 76 percent of the computational costs. The hardware is implemented with Verilog-HDL and synthesized with SYNOPSYS Design Compiler. TSMC 65nm cell library is adopted to design the hardware. The operation frequency is at 240MHz. The total gate count is 766K. We also verify the design with FPGA. The demo platform is based on Terasic DE4 development board with the Altera Stratix IV GX device. The FPGA demo system up-samples the video by the proposed super resolution hardware at the frame size of 1920x1080 and the frame rate of 24 frames per second. The results show that our architecture is able to provide high quality output in real-time while solving the problems of zigzag and blurred effects caused by conventional scaler.
en
dc.description.provenanceMade available in DSpace on 2021-06-17T00:30:27Z (GMT). No. of bitstreams: 1
ntu-101-R98943016-1.pdf: 25701138 bytes, checksum: 7a2b3a3b9713acfaede3f687c08f211b (MD5)
Previous issue date: 2012
en
dc.description.tableofcontents1 Introduction 1
1.1 Introduction to Super Resolution . . . . . . . . . . . . . . . . . . 1
1.2 ChallengesofConventionalTVScaler . . . . . . . . . . . . . . . 2
1.3 MotivationandDesignTarget . . . . . . . . . . . . . . . . . . . . 4
1.4 ThesisOrganization . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 Overview of Super Resolution Algorithms 5
2.1 Interpolation-BasedAlgorithms . . . . . . . . . . . . . . . . . . 5
2.2 Statistical-BasedAlgorithms . . . . . . . . . . . . . . . . . . . . 6
2.3 Learning-BasedAlgorithms . . . . . . . . . . . . . . . . . . . . . 7
2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3 Proposed Algorithm Based on Database-Free Texture Synthesis 11
3.1 Overview of the Proposed Algorithm . . . . . . . . . . . . . . . . 11
3.1.1 MainConcept . . . . . . . . . . . . . . . . . . . . . . . . 11
3.1.2 AlgorithmFlow. . . . . . . . . . . . . . . . . . . . . . . 12
3.2 Double Interpolation . . . . . . . . . . . . . . . . . . . . . . . . 12
3.2.1 Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2.2 Image Up-Sampling using DI-Difference . . . . . . . . . 15
3.3 TextureSynthesis . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3.1 Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.3.2 Database-FreeTextureSynthesis . . . . . . . . . . . . . . 20
3.3.3 CoefficientAnalysis . . . . . . . . . . . . . . . . . . . . 22
3.4 GradientDescent . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.5 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . . . 24
4 Proposed Hardware Architecture 29
4.1 Specification andDesignChallenges . . . . . . . . . . . . . . . . 30
4.2 Double Interpolation Architecture . . . . . . . . . . . . . . . . . 31
4.2.1 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.2.2 The Proposed Architecture . . . . . . . . . . . . . . . . . 35
4.3 GradientDescentArchitecture . . . . . . . . . . . . . . . . . . . 35
4.3.1 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.3.2 The Proposed Architecture . . . . . . . . . . . . . . . . . 40
4.4 TextureSynthesisArchitecture . . . . . . . . . . . . . . . . . . . 41
4.4.1 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 42
4.4.2 The Proposed Architecture . . . . . . . . . . . . . . . . . 44
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
5 Implementation Results 47
5.1 SynthesisResults . . . . . . . . . . . . . . . . . . . . . . . . . . 47
5.2 FPGADemoSystem . . . . . . . . . . . . . . . . . . . . . . . . 48
5.2.1 DemoSystemFlow . . . . . . . . . . . . . . . . . . . . . 48
5.2.2 DataReorderingArchitecture . . . . . . . . . . . . . . . 50
6 Conclusion 55
Reference 57
dc.language.isoen
dc.title利用超解析度技術之電視縮放控制器之演算法及硬體架構設計zh_TW
dc.titleAlgorithm and Hardware Architecture Design of Super Resolution Targeting TV Scaleren
dc.typeThesis
dc.date.schoolyear100-1
dc.description.degree碩士
dc.contributor.oralexamcommittee陳宏銘,盧奕璋,蔡宗漢
dc.subject.keyword超解析度技術,紋理合成技術,電視縮放控制器,硬體架構,zh_TW
dc.subject.keywordsuper resolution,texture synthesis,TV scaler,hardware architecture,en
dc.relation.page60
dc.rights.note有償授權
dc.date.accepted2012-02-13
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept電子工程學研究所zh_TW
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