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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/37003完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 貝蘇章(Soo-Chang Pei) | |
| dc.contributor.author | Hui-Chun Lien | en |
| dc.contributor.author | 連蕙君 | zh_TW |
| dc.date.accessioned | 2021-06-13T15:17:45Z | - |
| dc.date.available | 2011-07-15 | |
| dc.date.copyright | 2008-07-30 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-07-23 | |
| dc.identifier.citation | Chapter 2 Human Visual Perception
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Woods, Digital Image Processing second edition, Prentice Hall, 2002 [13] W. Pratt, Digital Image Processing fourth edition, WILEY, 2007 [14] T. Acharya, A. K. Ray, Image Processing Principles and Applications, WILEY, 2005 [15] K. S. Thyagarajan, Digital Image Processing with Application to Digital Cinema, Focal Press, 2006 Chapter 4 Local Image Enhancement: Retinex Chapter 4.2 Retinex Algorithm: Reflectance Only [16] D. J. Jobson, “Properties and performance of a center/surround Retinex,” IEEE Trans. Image Process., vol. 6, no. 3, pp. 451–462, Mar. 1997. [17] D. J. Jobson, Z. Rahman, and G. A. Woodell, “A multiscale Retinex for bridging the gap between color images and the human observation of scenes,” IEEE Trans. Image Process., vol. 6, no. 7, pp. 965–976, Jul. 1997. [18] Z. Rahman, D. J. Jobson, and G. A. Woodell, “Retinex processing for automatic image enhancement,” J. Electron. Imag., vol. 13, no. 1, pp. 100–110, 2004. [19] O. Zhou and J.P. Oakley, “Advantages of multiscale product filters for dynamic range compression in images,” IEEE Proc.-Vis. Image Signal Process., Vol. 153, No 6, pp. 851-859, Dec. 2006. [20] K. Barnard and B. Funt, “Investigations into multi-scale Retinex,” in Colour Imaging: Vision and Technology. New York:Wiley, 1999, pp. 9–17. [21] D. H. Choi, I. H. Jang and M. H. Kim N. C. Kim, “Color Image Enhancement Based on Single-Scale Retinex With a JND-Based Nonlinear Filter,” Circuits and Systems, 2007. ISCAS 2007. IEEE International Symposium on, pp. 3948-3951, May 2007. [22] B. Sun, W. Chen, H. Li, W. Tao and J. Li, “Modified Luminance Based Adaptive MSR.” Image and Graphics, 2007. ICIG 2007. Fourth International Conference on, pp. 116-120, Aug. 2007. Chapter 4.3 Retinex Algorithm: Illumination Adjustment [23] R. Kimmel , M. Elad , D. Shaked , R. Keshet and I. Sobel, “A Variational Framework for Retinex,” International Journal of Computer Vision, Springer Netherlands, Vol. 52, No. 1, Apr. 2003 [24] G. Orsini, G. Ramponi, P. Carrai and R. Di Federico, “A MODIFIED RETINEX FOR IMAGE CONTRAST,” Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on, Sep. 2003. Chapter 4.6 Image Enhancement in Poor Visibility Conditions [25] D. J. Jobson, Z. Rahman, and G. A. Woodell, “Feature visibility limits in the non-linear enhancement of turbid images,” Visual Information Processing XII, Proc. SPIE 5108, (2003) [26] G. A. Woodell, D. J. Jobson, Z. Rahman, G. D. Hines, “Enhancement of imagery in poor visibility conditions,” Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense IV, Proc. SPIE 5778, (2005) [27] R. T. Tan, N. Pettersson, L. Pettersson, “Visibility Enhancement for Roads with Foggy or Hazy Scenes,” IEEE intelligent Vehicles Symposium, Istanbul, Turkey, Jun. 13-15, 2007 [28] R. Rao and S. Lee, “ALGORITHMS FOR SCENE RESTORATION AND VISIBILITY ESTIMATION FROM AEROSOL SCATTER IMPAIRED IMAGES,” Image Processing, 2005. ICIP 2005. IEEE International Conference on, vol. 1, pp. I - 929-32, Sep. 2005. [29] H. Peng and R. Rao, “Image Enhancement of FOG-Impaired Scenes with Variable Visibility,” Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on, vol. 2, pp. II-389-II-392, Apr. 2007. Chapter 4.7 Applications of Image Enhancement [30] Z. Rahman, G. A. Woodell, and D. J. Jobson, “Retinex Image Enhancement: Application to Medical Images,” NASA workshop on New Partnerships in Medical Diagnostic Imaging, Greenbelt , Maryland, July 2001. [31] G. A. Woodell, Z. Rahman, D. J. Jobson, G. D. Hines, “Enhanced images for checked and carry-on baggage and cargo screening,” Sensors, and Command, Control, Communication, Proc. SPIE 5403, (2004) Chapter 4.8 Real-time Image Enhancement [32] S. Lee, “An Efficient Content-Based Image Enhancement in the compressed Domain Using Retinex Theory,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, no. 2, pp. 199-213, Feb. 2007 [33] G. D. Hines, Z. Rahman, D. J. Jobson, G. A. Woodell, “DSP implementation of the multiscale retinex image enhancement algorithm,” Visual Information Processing XIII, Proc. SPIE 5438, (2004) [34] G. D. Hines, Z. Rahman, D. J. Jobson, G. A. Woodell, “Real-time Enhancement, Registration, and Fusion for a Multi-Sensor Enhanced Vision System,” Enhanced and Synthetic Vision 2005, Proc. SPIE 6226, (2006) [35] S. Saponara, L. Fanucci, S. Marsi, G. Ramponi,, D. Kammler, and E. M. Witte, “Application-Specific Instruction-Set Processor for Retinex-Like Image and Video Processing,” Circuits and Systems II: Express Briefs, IEEE Transactions on, Vol. 54, Issue 7, pp. 596-600, July 2007. [36] S. Saponara, L. Fanucci, S. Marsi, G. Ramponi, “Algorithmic and architectural design for real-time and power-efficient Retinex image/video processing,” J. of Real-Time Image Processing, Vol. 1, No. 4, pp. 267-283, July 2007. Relate Research Website [37] NASA http://dragon.larc.nasa.gov/ [38] DICO http://dico.unimi.it/ [39] Computational Vision Laboratory http://www.cs.sfu.ca/~colour/ [40] TruView http://www.truview.com/ Reference Website [41] Light and Vision http://hyperphysics.phy-astr.gsu.edu/hbase/ligcon.html#ligcon [42] Vision http://www.unmc.edu/Physiology/Mann/mann7.html [43] Edwin H. Land -1 http://en.wikipedia.org/wiki/Edwin_Land [44] Edwin H. Land -2 http://www.nap.edu/html/biomems/eland.html [45] History of Color Photography http://old.photosharp.com.tw/photo123/history-8.htm | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/37003 | - |
| dc.description.abstract | 現今數位相機幾乎是每人必備的生活紀錄工具，但市面上的相機所拍攝的影像，卻時常跟我們眼睛所見到的不同。這些影像是可以由後製處理使其更美觀、清晰或接近真實所見，可是這通常需要人為的判斷與調整。如果要處理的影像數量龐大，或是想要讓一般不會影像後製的民眾也能獲得美觀的照片，亦或者希望把處理內建在相機中，都需要自動影像美化的技術。這篇論文的目標就是完成一個全自動的影像美化系統。
影像美化的方法主要就是調整亮度、對比，還有色彩的平衡。傳統自動影像美化的技術多半是整張影像作調整，容易有不自然的效果；而在這裡我們引進視網膜-大腦皮質(Retinex)技術，可以針對影像內容的不同，作局部的調整。我們在論文中實作並比較了一些研究提出的方法，整合各項技術的優點，經過一些測試圖片決定使用參數，最終建立一個全自動的系統。不需要人工手動調整，即可處理任何影像，達到暗部、亮部皆清晰且自然的結果，並解決強光下的陰影中細節不清晰，或濃霧中影像對比過低的問題。我們的自動影像美化技術相較於一些傳統技術，能夠讓各類型的影像皆能達到自然美觀的結果，局部的對比與整體的色調都有不錯的表現。這篇論文的研究讓全自動美化系統不再是遙不可及的夢想。 | zh_TW |
| dc.description.abstract | Nowadays digital camera is a necessary tool for people to record their daily life, but images taken by current cameras are usually different from the realistic view. We can use some image post-process techniques to get more pleasant, clearer image or make it nearer to the realistic view, but these techniques need manual adjustment. If there are a large amount of images need to be enhanced, then manual adjustment is not practical; another truth is that not everyone can do image post-process. Thus an automatic image enhancement technique can be very helpful, especially an embedded automatic processor for camera. Our goal is to develop an automatic image enhanced system.
Basic image enhancement is accomplished by adjusting the intensity, the contrast, and the color balance. The conventional automatic image enhancements use global adjustment. Here, we introduce Retinex method to perform the local image enhancement. It can adjust images locally depending on the content of the image. We implement and compare several published algorithms. By combining the advantages of these algorithms and determining the parameters by testing them on known images, we implement an automatic system successfully. It can enhance images without any manual control or adjustment. It is robust enough to deal with almost any kind of images. Those fine details in dark and bright region are both revealed clearly after process. The unclear part in the shadow of sunlight and the low contrast of foggy image can be resolved by our system. It can deal with much more varieties of images as compared to the conventional techniques. It also gives a pleasant, nature image. The local contrast and tonal rendition are both quiet good. This study shows that automatic image enhanced system can be a reality and no longer be an unreachable dream. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T15:17:45Z (GMT). No. of bitstreams: 1 ntu-97-R95942024-1.pdf: 7727696 bytes, checksum: d5c71732a62e8cccb44725fa7f59c05d (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | Abstract..............................iii
List of Figures..............................vii List of Tables..............................xi CHAPTER 1 Introduction..............................1 CHAPTER 2 Human Visual Perception..............................5 2.1 Introduction..............................5 2.2 Main functions of Human Eye..............................7 2.3 Light and Visual Phenomena..............................11 2.3.1 Dynamic Range of Human Visual System and Brightness Adaptation..............................11 2.3.2 Contrast Sensitivity..............................13 2.3.3 Mach Bands and Simultaneous Contrast..............................13 2.4 Image Formation Model..............................15 2.5 Land’s Retinex Theory..............................15 2.6 Conclusion..............................19 CHAPTER 3 Global Image Enhancement: Log-transformation, Gamma-Correction, Histogram Equalization and Autolevels..............................23 3.1 Introduction..............................23 3.2 Image Negatives..............................25 3.3 Log Transformations..............................25 3.4 Power-Law transformation and Gamma Correction..............................28 3.5 Histogram Processing..............................31 3.5.1 Histogram Equalization..............................32 3.5.2 Adaptive Histogram Equalization..............................35 3.6 Contrast Stretching and Autolevels..............................36 3.7 Conclusion..............................41 CHAPTER 4 Local Image Enhancement: Retinex..............................45 4.1 Introduction..............................45 4.2 Retinex Algorithm: Reflectance Only..............................49 4.2.1 Single-Scale Retinex (SSR)..............................49 4.2.2 Multi-Scale Retinex (MSR)..............................53 4.2.3 Multi-Scale Retinex with Color Restoration (MSRCR)..............................56 4.2.4 Multi-Scale Product Filter (MSPF)..............................59 4.2.5 Chromaticity Preserving MSR (CPMSR)..............................62 4.2.6 Multi-Scale Retinex (MSR) in Various Color Space..............................63 4.3 Retinex Algorithm: Illumination Adjustment..............................64 4.3.1 Multi-Scale Retinex-Based Gamma Correction (MSR-G)..............................65 4.4 Summary of Various Retinex Algorithm..............................70 4.5 Comparison of Various Retinex Algorithm..............................79 4.6 Image Enhancement in Poor Visibility Conditions..............................83 4.7 Applications of Retinex Algorithm..............................88 4.8 Real-time Retinex System..............................88 4.9 Conclusion..............................89 CHAPTER 5 Automatic Multi-Scale Retinex-Based Gamma Correction (AMSR-G)..............................93 5.1 Introduction..............................93 5.2 Decision of Block Process in Automatic MSR-G system..............................93 5.3 Comparison with Other Techniques..............................98 5.4 Conclusion..............................102 CHAPTER 6 Conclusion and Feature Work..............................105 6.1 Conclusion..............................105 6.2 Future Work..............................106 Reference..............................109 Appendix..............................117 | |
| dc.language.iso | en | |
| dc.title | 以視網膜-大腦皮質為基礎的智慧型影像處理技術 | zh_TW |
| dc.title | Retinex-Based Intelligent Image Enhancement Technologies | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 杭學鳴(Hsueh-Ming Hang),黃文良(Wen-Liang Hwang),郭景明(Jing-Ming Guo) | |
| dc.subject.keyword | 人類視覺,動態範圍壓縮,多規模視網膜-大腦皮質技術,自動影像美化, | zh_TW |
| dc.subject.keyword | Human vision,dynamic range compression,multi-scale retinex (MSR),automatic image enhancement, | en |
| dc.relation.page | 130 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2008-07-25 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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