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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 貝蘇章 | |
dc.contributor.author | Wan-Lin Su | en |
dc.contributor.author | 蘇宛琳 | zh_TW |
dc.date.accessioned | 2021-06-17T03:17:20Z | - |
dc.date.available | 2018-07-06 | |
dc.date.copyright | 2018-07-06 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-07-03 | |
dc.identifier.citation | Reference
[1] Chien-Cheng Tseng and Su-Ling Lee, 'A Fusion Method for Color Image Enhancement in Uniform Color Space', The 30th IPPR Conference on Computer Vision, Graphics, and Image Processing (CVGIP 2017), Aug. 2017, pp. 1-8, Nantou. [2] Chien-Cheng Tseng, Su-Ling Lee, and Cheng-Yu Wen, 'Digital Color Image Enhancement Methods Using Various Color Saturations', The 29th IPPR Conference on Computer Vision, Graphics, and Image Processing (CVGIP 2016), Aug. 2016, pp. 1-8, Keelung. [3] Fig 2.1: The poorly contrasted night-time image https://www.youtube.com/watch?v=mRjo3esOWvs [4] Fig 2.2(1): The CIEXYZ color space https://commons.wikimedia.org/wiki/File:SRGB_gamut_within_CIEXYZ_color_space_isosurface.png [5] Fig 2.2(2): The CIEXYZ color space within D65 whitepoint https://commons.wikimedia.org/wiki/File:Visible_gamut_within_CIEXYZ_color_space_D65_whitepoint_mesh.png [6] Fig 2.2.1(1): The CIELAB color space https://commons.wikimedia.org/wiki/File:SRGB_gamut_within_CIELAB_color_space_isosurface.png [7] Fig 2.2.1(2): The CIELAB color space within D65 whitepoint https://commons.wikimedia.org/wiki/File:Visible_gamut_within_CIELAB_color_space_D65_whitepoint_mesh.png [8] Fig 2.2.1(3): The CIELCH of the CIELAB color space within D65 whitepoint https://commons.wikimedia.org/wiki/File:Visible_gamut_within_CIELCHab_color_space_D65_whitepoint_mesh.png [9] Fig 2.2.2(1): The CIELUV color space https://commons.wikimedia.org/wiki/File:SRGB_gamut_within_CIELUV_color_space_isosurface.png [10] Fig 2.2.2(2): The CIELUV color space within D65 whitepoint https://commons.wikimedia.org/wiki/File:Visible_gamut_within_CIELUV_color_space_D65_whitepoint_mesh.png [11] Fig 2.2.2(3): The CIELCH of the CIELUV color space within D65 whitepoint https://commons.wikimedia.org/wiki/File:Visible_gamut_within_CIELCHuv_color_space_D65_whitepoint_mesh.png [12] Fig 2.2.2(4): The gamut of the SRGB color space https://github.com/nschloe/colorio/blob/gh-pages/srgb-gamut-srgb.png [13] Fig 2.2.2(5): The gamut of the CIEXYZ color space https://github.com/nschloe/colorio/blob/gh-pages/srgb-gamut-xyz.png [14] Fig 2.2.2(6): The gamut of the CIELAB color space https://github.com/nschloe/colorio/blob/gh-pages/srgb-gamut-cielab.png [15] Fig 2.2.2(7): The gamut of the CIELUV color space https://github.com/nschloe/colorio/blob/gh-pages/srgb-gamut-cieluv.png [16] Fig 2.3: The IPT color space http://www.lamptolaser.com/fact7.html [17] Fig 2.3.1(2): The relationship of ICTCP color space to the Human Visual System https://www.dolby.com/us/en/technologies/dolby-vision/ictcp-white-paper.pdf [18] Khan, Muhammad Usman, et al. 'Performance Comparison of Uniform Color Spaces by Integrating into a Tone Mapping Operator.' Applied Sciences in Graphic Communication and Packaging. Springer, Singapore, 2018. 39-45. [19] Dolby, “ What is ICTCP – Introduction? ” White paper, version 7.1 (Dolby, United States, 2016). [20] Fig 2.3.1(3): The LMS curve https://en.wikipedia.org/wiki/LMS_color_space#/media/File:Cones_SMJ2_E.svg [21] Fig 2.4(3): Enhance red image data set (original) https://liddleworks.com/brighton-college-online-marketing-social-media/underexposed-copy-2/?doing_wp_cron=1525223607.5415799617767333984375 https://c.pxhere.com/photos/02/76/apple_red_wood_fruit_healthy_green_dieting_fresh_fruit-613628.jpg!d https://c.pxhere.com/photos/90/9e/apple_red_wood_fruit_healthy_green_dieting_fresh_fruit-613766.jpg!d [22] Fig 2.4(4): Enhance green image data set (original) https://c.pxhere.com/photos/a2/33/spinach_healthy_green_dieting_greens_diet_eating_nutrition-613627.jpg!d https://c.pxhere.com/photos/cc/b2/top_view_closeup_vegetarianism_healthy_eating_vegetables_green_cocktail_bubbles-816314.jpg!d https://c.pxhere.com/photos/53/76/grapes_frozen_fruit_summer_organic_fresh_healthy_food_diet-600076.jpg!d [23] Fig 2.4(5): Enhance blue image data set (original) https://c.pxhere.com/photos/60/af/berries_blueberries_blackberries_fruits_vitamins_blue_delicious_healthy-517761.jpg!d https://c.pxhere.com/photos/51/d5/berries_bilberries_blueberries_food_fruits_healthy_huckleberries-1168002.jpg!d https://c.pxhere.com/photos/9c/c9/blueberries_fruit_food_berries_blue_organic_diet_ripe-893392.jpg!d https://c.pxhere.com/photos/0c/fd/berries_frozen_food_consered_preserved_ice_fresh_sweet-1226685.jpg!d [24] Fig 2.4(6): Enhance colorful image data set (original) https://www.flickr.com/photos/9422878@N08/9364320043 https://c.pxhere.com/photos/1c/08/fruit_vitamins_health_sweet_bananas_pear_pears_apples-1049844.jpg!d https://c.pxhere.com/photos/58/52/apples_garden_wooden_desk_still_life_apple_orchard_apple_fruit_ripe-1193306.jpg!d [25] Liu, Qiegen, et al. 'GcsDecolor: gradient correlation similarity for efficient contrast preserving decolorization.' IEEE Transactions on Image Processing 24.9 (2015): 2889-2904. [26] Fig 3.1: The applications of color-to-gray conversion https://www.gmrwebteam.com https://www.shareicon.net/pictures-gallery-camera-photos-image-photo-111475 https://www.iconfinder.com/icons/905645/print_printer_printing_printout_icon#size=512 [27] Liu, Qiegen, et al. 'Log-Euclidean Metrics for Contrast Preserving Decolorization.' IEEE Transactions on Image Processing 26.12 (2017): 5772-5783. [28] Soo-Chang Pei, Wan-Lin Su, and Chiou-Shann Fuh, 'LOCAL AND GLOBAL CONTRAST PRESERVING DECOLORIZATION USING GRADIENT MATRIX CORRELATION', The 30th IPPR Conference on Computer Vision, Graphics, and Image Processing (CVGIP 2017), Aug. 2017, pp. 1-8, Nantou. [29] J. G. Kuk, J. H. Ahn, and N. I. Cho, “A color to grayscale conversion considering local and global contrast,” in Proc. ACCV, vol. 4. 2010, pp. 513–524. [30] C. Lu, L. Xu, and J. Jia, “Contrast preserving decolorization,” in Proc. IEEE Int. Conf. Comput. Photography (ICCP), Apr. 2012, pp. 1–7. [31] M. Cˆadík, “Perceptual evaluation of color-to-grayscale image conversions,” Comput. Graph. Forum, vol. 27, no. 7, pp. 1745–1754, Oct. 2008. [32] The Berkeley Segmentation Dataset (BSDS) http://download-wallpaper.net/blog/images-nature.html [33] R. Bala and R. Eschbach, “Spatial color-to-grayscale transform preserving chrominance edge information,” in Proc. Color Imag. Conf., 2004, pp. 82–86. [34] K. Smith, P.-E. Landes, J. Thollot, and K. Myszkowski, “Apparent greyscale: A simple and fast conversion to perceptually accurate images and video,” Comput. Graph. Forum, vol. 27, no. 2, pp. 193–200, Apr. 2008. [35] A. A. Gooch, S. C. Olsen, J. Tumblin, and B. Gooch, “Color2Gray: Salience-preserving color removal,” ACM Trans. Graph., vol. 24, no. 3, pp. 634–639, Jul. 2005. [36] C. Lu, L. Xu, and J. Jia, “Contrast preserving decolorization,” in Proc. IEEE Int. Conf. Comput. Photography (ICCP), Apr. 2012, pp. 1–7. [37] C. Lu, L. Xu, and J. Jia, “Real-time contrast preserving decolorization,” in Proc. SIGGRAPH Asia Tech. Briefs, 2012, pp. 1–4. [38] Borland, David, and Russell M. Taylor Ii. 'Rainbow color map (still) considered harmful.' IEEE computer graphics and applications 27.2 (2007). [39] THE RAINBOW IS DEAD…LONG LIVE THE RAINBOW! – PERCEPTUAL PALETTES, PART 1 https://mycarta.wordpress.com/2012/05/12/the-rainbow-is-dead-long-live-the-rainbow-part-1/ [40] Fig 4.1(1): The image with grayscale, heated, and rainbow-like palette https://mycarta.wordpress.com/2012/05/12/the-rainbow-is-dead-long-live-the-rainbow-part-1/ [41] Fig 4.1(2): The intensity information of grayscale, heated, and rainbow-like palette https://mycarta.files.wordpress.com/2011/11/bust_converted_gray.png [42] Fig 4.2(1): The rainbow-like palette pyramid https://mycarta.files.wordpress.com/2011/11/natural-spectrum1.png [43] Fig 4.2(2): The lightness L* behavior of the rainbow-like palette https://mycarta.files.wordpress.com/2011/11/natural-spectrum1.png [44] THE RAINBOW IS DEAD…LONG LIVE THE RAINBOW! – THE RAINBOW IS DEAD…LONG LIVE THE RAINBOW! – PERCEPTUAL PALETTES, PART 4 – CIE LAB HEATED BODY https://mycarta.wordpress.com/2012/10/14/the-rainbow-is-deadlong-live-the-rainbow-part-4-cie-lab-heated-body/ [45] Fig 4.2.1(1): The image with grayscale and heated body palette https://mycarta.files.wordpress.com/2011/11/bust_koenderink.png [46] Fig 4.2.1(2): The intensity information of grayscale and heated body palette https://mycarta.files.wordpress.com/2011/11/bust_converted_gray.png [47] Fig 4.2.1(3): The CIELAB Heated Body palette pyramid https://mycarta.files.wordpress.com/2012/06/pyramid-hot1.png [48] Fig 4.2.1(4): The lightness L* behavior of the CIELAB Heated Body palette https://mycarta.files.wordpress.com/2012/06/pyramid-hot2.png [49] Fig 4.2.1(5): The linearly increasing lightness L* of the CIELAB Heated Body palette https://mycarta.files.wordpress.com/2012/06/l-plot-for-hot-linear-l.png [50] Fig 4.2.1(6): The compared CIELAB Heated Body palette pyramid https://mycarta.files.wordpress.com/2012/06/pyramid-hot-both.png [51] THE RAINBOW IS DEAD…LONG LIVE THE RAINBOW! – PERCEPTUAL PALETTES, PART 5 – CIE LAB LINEAR L* RAINBOW https://mycarta.wordpress.com/2012/12/06/the-rainbow-is-deadlong-live-the-rainbow-part-5-cie-lab-linear-l-rainbow/ [52] Kindlmann, G. Reinhard, E. and Creem, S., 2002, Face-based Luminance Matching for Perceptual Colormap Generation, IEEE – Proceedings of the conference on Visualization ’02 [53] Fig 4.2.2(1): The HSL linear L rainbow palette (Kindlmann color palette) https://mycarta.files.wordpress.com/2012/08/linearl_face_based.png [54] Fig 4.2.2(2): The lightness L* behavior of the Kindlmann color palette https://mycarta.files.wordpress.com/2012/08/l-plot-initial-linearl.png [55] Fig 4.2.2(3): The Kindlmann color palette pyramid https://mycarta.files.wordpress.com/2012/11/pyramid-linearl1.png [56] Fig 4.2.2(4): The linearly increasing lightness L* of the Cube Law Rainbow palette https://mycarta.files.wordpress.com/2012/08/l-plot-adjusted-linearl.png [57] Fig 4.2.2(5): The Cube Law Rainbow palette pyramid https://mycarta.files.wordpress.com/2012/11/pyramid-linearl_adjusted.png [58] Fig 4.3(1): The visible natural spectrum color rainbow palette https://mycarta.files.wordpress.com/2012/05/roygbiv_interpolated.png [59] Fig 4.3(2): The lightness L* behavior of the natural spectrum color rainbow palette https://mycarta.files.wordpress.com/2012/06/lplot_roygbiv2.png [60] Fig 4.3.1(1): The perceived intensity of visible natural spectrum color rainbow palette https://mycarta.files.wordpress.com/2012/05/roygbiv.png https://mycarta.files.wordpress.com/2012/05/roygbiv_intensity_sorted1.png [61] Fig 4.3.1(2): The intensity difference of YOGRVIB color palette https://mycarta.files.wordpress.com/2012/05/roygbiv_intensity_sorted1.png https://mycarta.files.wordpress.com/2012/05/yogrvib.png [62] Fig 4.3.1(3): The interpolation of YOGRVIB color palette https://mycarta.files.wordpress.com/2012/05/yogrvib.png https://mycarta.files.wordpress.com/2012/05/yogrvib_interpoloated1.png [63] THE RAINBOW IS DEAD…LONG LIVE THE RAINBOW! – THE RAINBOW IS DEAD…LONG LIVE THE RAINBOW! – PERCEPTUAL PALETTES, PART 2: A RAINBOW PUZZLE https://mycarta.wordpress.com/2012/05/28/the-rainbow-is-dead-long-live-the-rainbow-part-2-a-rainbow-puzzle/ [64] THE RAINBOW IS DEAD…LONG LIVE THE RAINBOW! – THE RAINBOW IS DEAD…LONG LIVE THE RAINBOW! – PERCEPTUAL PALETTES, PART 3 https://mycarta.wordpress.com/2012/10/06/the-rainbow-is-deadlong-live-the-rainbow-part-3/ [65] Fig 4.3.1(6): The intensity difference of YOGRVIB color palette https://mycarta.files.wordpress.com/2012/05/roygbiv_intensity_sorted1.png https://mycarta.files.wordpress.com/2012/05/yogrvib.png [66] Fig 4.3.2(10): The hue discrimination curve with the smallest observable hue difference https://mycarta.files.wordpress.com/2012/06/gregory_wavelength_discrimination.png [67] Fig 4.4(1): G-to-C conversion methods’ comparison of HEAD x-ray images https://radiopaedia.org/cases/cervical-spine-disc-spacer [68] Fig 4.4(2): G-to-C conversion methods’ comparison of SPINE-1 x-ray images https://radiopaedia.org/cases/dagger-sign-in-ankylosing-spondylitis [69] Fig 4.4(3): G-to-C conversion methods’ comparison of SPINE-2 x-ray images https://radiopaedia.org/cases/bilateral-nephrostomy-tubes [70] Fig 4.4(4): G-to-C conversion methods’ comparison of HAND-1 x-ray images https://radiopaedia.org/cases/boxer-fracture-6 [71] Fig 4.4(5): G-to-C conversion methods’ comparison of HAND-2 x-ray images https://mycarta.files.wordpress.com/2011/10/best.png [72] Fig 4.4(6): G-to-C conversion methods’ comparison of LEG x-ray images https://radiopaedia.org/cases/posterior-acetabular-fracture [73] smartphone icon, camera icon, video icon https://thenounproject.com/chrystina99angeline/uploads/?i=1598553 https://thenounproject.com/chrystina99angeline/uploads/?i=1482523 https://thenounproject.com/chrystina99angeline/uploads/?i=1647442 [74] printer icon https://www.iconfinder.com/icons/905645/print_printer_printing_printout_icon#size=512 [75] palette and paintbrush icon https://thenounproject.com/search/?q=palette&i=1417920 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69495 | - |
dc.description.abstract | 隨者科技時代的到來,電腦視覺被廣泛地應用在許多研究領域,如何完整地保留影像資訊,以及如何清楚地呈現及表達數據資料,都是值得我們深入研究的議題。在做影像處理或電腦視覺特效前,影像增強是不可或缺的步驟之一,經由增強影像的步驟能更有效地在後續的彩色影像轉灰階影像的處理中保留更多的對比,我運用兩個新的色彩空間IC_P C_T以及J_z a_z b_z,來開發影像增強的演算法並進行背光模式測試以及夜視相機的實驗。此外,在彩色影像轉灰階影像的脫色處理中則是利用梯度矩陣相關的方法,有效率地計算廣域及鄰域範圍間像素的對比關係,來保留原本在彩色影像中的顏色特徵,讓轉換完的灰階影像能保留更多有用的細節資訊。
最後,注重在開發符合人眼視覺感知系統之色盤,因為現在被大家廣為使用的rainbow color map色盤有視覺感知排序缺陷、灰階亮度非線性變化及錯誤的方向向量內插等問題,為了解決這些問題,我保留了rainbow color map的顏色資訊並應用人眼對不同顏色變化最小可分辨的差異度,開發了一個新的符合人眼視覺感知系統的色盤 - Perceptual YORVBI Color Palette,我提出的新色盤不只符合人眼感知,更能清楚地呈現及表達數據資料。 | zh_TW |
dc.description.abstract | With the advent of the technological era, computer vision is widely used in many research fields. So how to efficiently and completely keep the details of image information, and also clearly demonstrate and represent the research data are all worthwhile to deeply study, do research and pay more attention to. Before image processing, image enhancement is an indispensable step, which can efficiently retain more details of the contrast feature in color-to-gray conversion. I utilized two new perceptual color spaces IC_P C_T and J_z a_z b_z to develop image enhancement algorithm and do experiments including backlight mode enhancement, and night vision camera implementation. In addition, Gradient Matrix Correlation (GmcDecolor) method which I proposed in color-to-gray conversion can efficiently preserve the contrast feature between both global and local contrast of each pixel in the decolorization processing.
Last, I put emphasis on the topic about developing a perceptual color palette based on human visual system. Since the widely used rainbow color map confuses viewers through its lack of perceptual ordering, obscures data through its uncontrolled luminance variation, and actively misleads interpretation through the introduction of non-data-dependent gradients. In order to solve these problems, I retain the hue information of rainbow color map, and utilize the hue discrimination observation with the smallest observable hue difference. With the above improvements, I propose a new perceptual YORVBI color palette which can clearly demonstrate and represent the research data. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T03:17:20Z (GMT). No. of bitstreams: 1 ntu-107-R05942060-1.pdf: 11252072 bytes, checksum: 5c3876954f55a330a1d5251c07f80e50 (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | CONTENTS
口試委員會審定書 ii 誌謝 iii 中文摘要 iv ABSTRACT v CONTENTS vi LIST OF FIGURES viii Chapter 1 Introduction 1 1.1 Image Enhancement in Computer Vision 1 1.2 Algorithms for C-to-G and G-to-C Conversion 1 Chapter 2 Image Enhancement in Uniform Color Space 2 2.1 Introduction 2 2.2 Related Work of Image Enhancement 3 2.2.1 Enhancement Method in CIELAB Color Space 4 2.2.2 Enhancement Method in CIELUV Color Space 9 2.3 Proposed Method of Image Enhancement 15 2.3.1 Image Enhancement in ICTCP Color Space 16 2.3.2 Image Enhancement in Jzazbz Color Space 22 2.4 Experiment Results 28 2.5 Conclusions 38 Chapter 3 Efficient Contrast Preserving Method for Decolorization 39 3.1 Introduction 39 3.2 Related Work of Color-to-Gray Conversion 40 3.2.1 GcsDecolor : Gradient Correlation Similarity 41 3.2.2 LeDecolor : Log-Euclidean Metrics 45 3.3 Proposed Method of Color-to-Gray Conversion 50 3.3.1 GmcDecolor : Gradient Matrix Correlation 50 3.3.2 Sensitive Weighted Selection 54 3.4 Experiment Results 56 3.5 Conclusions 66 Chapter 4 Perceptual Color Palette Designed from Human Visual System 67 4.1 Introduction 67 4.2 Related Work of Color Palette 69 4.2.1 CIELAB Heated Body 70 4.2.2 Cube Law Rainbow 73 4.3 Proposed Method of Perceptual Color Palette 76 4.3.1 Perceptual Color Palette - Linear_YGORVBI 78 4.3.2 Perceptual Color Palette - Linear_YORVBI 84 4.4 Experiment Results 87 4.5 Conclusions 93 Chapter 5 Application 94 5.1 Application 94 5.1.1 BackLight Mode and Night Vision Camera 94 5.1.2 Color-to-Gray Printer Application 100 5.1.3 X-ray images colorizing Demonstration 105 Reference 107 | |
dc.language.iso | en | |
dc.title | 影像增強、符合人眼視覺感知系統之彩色轉灰階影像及灰階轉彩色影像處理 | zh_TW |
dc.title | Image Enhancement, Perceptual Decolorization and Colorization Method based on Human Visual System | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 鐘國亮,杭學鳴,林康平 | |
dc.subject.keyword | 影像增強,背光模式,夜視相機,彩色轉灰階影像,灰階轉彩色影像, | zh_TW |
dc.subject.keyword | image enhancement,backlight mode enhancement,night vision camera,Color-to-Gray,Gray-to-Color, | en |
dc.relation.page | 118 | |
dc.identifier.doi | 10.6342/NTU201801234 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-07-03 | |
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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