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Author: 劉秉睿
Ping-Juei Liu
Thesis Title: 去霧演算法之亮度與對比增強研究
A study of the contrast and luminance enhancement associated with haze-removal algorithms
Advisor: 洪西進
Shi-Jinn Horng
Committee: 楊昌彪
Chang-Biau Yang
楊竹星
Chu-Sing Yang
林灶生
Jzau-sheng Lin
李正吉
Cheng-Chi Lee
謝仁偉
Jen-Wei Hsieh
范欽雄
Chin-Shyurng Fahn
洪西進
Shi-Jinn Horng
Degree: 博士
Doctor
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2022
Graduation Academic Year: 110
Language: 英文
Pages: 112
Keywords (in Chinese): 霧霾去霧影像增強影像還原影像處理人工智慧
Keywords (in other languages): fog, haze, dehaze, image enhancement, image restoration, image processing, artificial intelligence
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  • 本研究提出一組兼顧影像還原的準確度,又能夠強化影像的亮度、對比、與細節等視覺品質的演算法組合。此組合包含新的去霧與強化演算法。去霧演算法是一種影像還原演算法;因此,還原的準確度是評測去霧演算法效能優劣的重要指標。傳統的影像強化法通常只顧及上述的視覺品質而無法確保還原的準確度。從誤差的角度而言,去霧影像常常在顏色上過度飽和;與此同時,受到霧氣影響而變得朦朧的區域卻無法完全清晰化。然而,傳統的影像強化法常常放大上述的誤差,造成相應的還原準確度下降。因此,本研究重新分析去霧演算法,展示去霧演算法的細微調整如何影響其結果的亮度與對比;並藉此建立符合本研究目標的演算法。我們的去霧演算法以對比與亮度為主要導向,因此更易於校調去霧影像的品質;而影像強化法能在不損及還原準確度的前提下提高影像的視覺品質。除此之外,本研究中所提出的去霧演算法,其時間複雜度屬於 O(nlog⁡(n));而影像強化法的複雜度也僅僅是 O(n),並且可以搭配任何去霧演算法使用。


    In this dissertation, a combination of algorithms that are capable to keep the restoration accuracy while enhancing the luminance, contrast, and detail of dehazed images are proposed. The combination includes a new haze-removal algorithm and a new enhancement method. Haze-removal algorithms are one kind of image-restoration algorithms; the restoration accuracy is one of the most important subjects to evaluate the algorithms. Conventional enhancement methods usually focus on the quality associated with the human visual perception rather than the restoration accuracy. From the perspective of errors, dehazed images usually suffer from the over-saturated and hazy regions at the same time; however, the conventional methods usually amplify at least one of the corresponding errors, harming corresponding restoration accuracy. Therefore, haze-removal algorithms are analyzed in this study to demonstrate how adjustments of the algorithms affect the luminance and contrast of corresponding dehazed images; in this way, we design algorithms that meet the above-mentioned demands. The proposed haze-removal algorithm is luminance and contrast-oriented and is more intuitive to be adjusted; besides, our enhancement method is eligible to keep the restoration accuracy while boosting the quality of dehazed images. Moreover, the time complexity of the proposed haze-removal algorithms and enhancement method is O(nlog⁡(n)) and O(n), respectively, and the enhancement method can collaborate with any haze-removal algorithm.

    論文摘要 I Abstract II 誌謝 III Contents IV List of Algorithms VI List of Figures VII List of Tables VIII Chapter one Introduction 1 1.1 Background 1 1.2 Motivation and Contribution 5 Chapter 2 Related Works 7 2.1 Dark Channel Prior 7 2.1.1 Algorithm of Dark Channel Prior 7 2.1.2 Analysis Associated with Dark Channel Prior 9 2.1.3 Refining Algorithm 10 2.2 Enhancement Method 11 Chapter 3 The Proposed Method 12 3.1 The Proposed Haze-Removal Algorithm 12 3.1.1 Preliminary Analysis 12 3.1.2 Our Haze-Removal Algorithm 15 3.1.3 Automatic Parameter Estimator 24 3.1.4 Time Complexity of Our Haze-removal Algorithm 25 3.1.5 Algorithm of Our Haze-removal Method 25 3.2 The Proposed Enhancement Method 26 3.2.1 Error Propagation in Haze-Removal Task 28 3.2.2 Our Enhancement Method 29 3.2.3 Additional Edge Verification Method 32 3.2.4 Time Complexity of Our Enhancement Method 38 3.2.5 Algorithms of Our Enhancement Method 38 3.3 Our Atmospheric Estimator 39 Chapter 4 Experimental Results 42 4.1 Detail of Environment and Parameter 42 4.2 Evaluation of Our Haze-Removal Algorithm 45 4.2.1 Evaluation Associated with Luminance and Contrast 45 4.2.2 Evaluation of Restoration Accuracy and Enhancement Performance 49 4.3 Evaluation of Our Enhancement Method 53 4.3.1 Comparison with Conventional Enhancement Method 53 4.3.2 Evaluation of Restoration Accuracy 57 4.3.3 Evaluation of Enhancement Performance 59 4.3.4 More Comparison 61 Chapter 5 Conclusion 64 Bibliography 65 Appendix 75

    [1] U. Brummund and B. Mesnier, “A comparative study of planar mie and rayleigh scattering for supersonic flowfield diagnostics,” in Instrumentation in Aerospace Simulation Facilities, 1999. ICIASF 99. 18th International Congress on, 1999, pp. 42/1–4210.
    [2] H. He, Y. Qin, and Q. Zheng, “Rayleigh, Mie, and tyndall scatterings of polystyrene microspheres in water: wavelength, size, and angle dependences,” in Journal of Applied Physics, vol. 105, no. 2, pp. 023110-1–023110-10, Jan. 2009.
    [3] Y. Y. Schechner and N. Karpel, “Recovery of underwater visibility and structure by polarization analysis,” in IEEE Journal of Oceanic Engineering, vol. 30, no. 3, pp. 570–587, Jul. 2005.
    [4] H. Koschmider, “Theorie der horizontalen sichtweite,” in Beitrage Zur Physik Der Freien Atmosphare, 1924, pp. 33–53.
    [5] R. Tan, “Visibility in bad weather from a single image,” in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, June 2008, pp. 1–8.
    [6] J. P. Tarel, N. Hautiere, L. Caraffa, A. Cord, H. Halmaoui, and D. Gruyer,“Vision enhancement in homogeneous and heterogeneous fog,” in IEEE Intelligent Transportation Systems Magazine., vol. 4, no. 2, pp. 6–20, Apr. 2012.
    [7] K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” in IEEE Transactions on Pattern Analysis and Machine Intelligence., vol. 33, no. 12, pp. 2341–2353, Dec. 2011.
    [8] G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient image dehazing with boundary constraint and contextual regularization,” in International Conference on Computer Vision (ICCV), Dec. 2013, pp. 617–624.
    [9] R. Fattal, “Dehazing using color-lines,” ACM Trans. Graph., vol. 34, no. 1, p. 13, 2014.
    [10] D. Berman, T. Treibitz, and S. Avidan, “Non-local image dehazing,” in IEEE Conference on Computer Vision and Pattern Recognition, Jun. 2016, pp. 1674–1682.
    [11] Q. Zhu, J. Mai and L. Shao, "A fast single image haze removal algorithm using color attenuation prior," in IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3522-3533, Nov. 2015, doi: 10.1109/TIP.2015.2446191.
    [12] B. Cai, X. Xu, K. Jia, C. Qing and D. Tao, "DehazeNet: an end-to-end system for single image haze removal," in IEEE Transactions on Image Processing, vol. 25, no. 11, pp. 5187-5198, Nov. 2016, doi: 10.1109/TIP.2016.2598681.
    [13] W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M. Yang. “Single image dehazing via multi-scale convolutional neural networks,” in European Conference on Computer Vision (ECCV), 2016.
    [14] B. Li, X. Peng, Z. Wang, J. Xu, and D. Feng, “Aod-net: All-in-one dehazing network,” in Proceedings of IEEE International Conference on Computer Vision, Oct. 2017, pp. 4780–4788.
    [15] W. Ren, L. Ma, J. Zhang, J. Pan, X. Cao, W Liu, and M. Yang, “Gated fusion network for single image dehazing,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
    [16] 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), 2018.
    [17] H. Dong et al., "Multi-scale boosted dehazing network with dense feature fusion," in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 2154-2164, doi: 10.1109/CVPR42600.2020.00223.
    [18] X. Liu, H. Li and C. Zhu, "Joint contrast enhancement and exposure fusion for real-world image dehazing," in IEEE Transactions on Multimedia, doi: 10.1109/TMM.2021.3110483.
    [19] C. Lin, X. Rong and X. Yu, "MSAFF-Net: multiscale attention feature fusion networks for single image dehazing and beyond," in IEEE Transactions on Multimedia, doi: 10.1109/TMM.2022.3155937.
    [20] J. Shin, H. Park and J. Paik, "Region-based dehazing via dual-supervised triple-convolutional network," in IEEE Transactions on Multimedia, vol. 24, pp. 245-260, 2022, doi: 10.1109/TMM.2021.3050053.
    [21] B. Li, W. Ren, D. Fu, D. Tao, D. Feng, W. Zeng, and Z. Wang, “Benchmarking single-image dehazing and beyond,” in IEEE Transactions on Image Processing, vol. 28, no. 1, pp. 492-505, Jan. 2019.
    [22] A. Chandra, A. Singh, R. Kumar and N. Dey, "Dehazing of aerial images by dark channel and gamma correction," in International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE), 2018, pp. 1-7, doi: 10.1109/ICRAIE.2018.8710428.
    [23] H. -M. Hu, Q. Guo, J. Zheng, H. Wang and B. Li, "Single image defogging based on illumination decomposition for visual maritime surveillance," in IEEE Transactions on Image Processing, vol. 28, no. 6, pp. 2882-2897, June 2019, doi: 10.1109/TIP.2019.2891901.
    [24] P. K. Sonkar and K. Raj, "Single image dehazing using dark channel prior with median filter and contrast enhancement," in IEEE International Conference for Innovation in Technology (INOCON), 2020, pp. 1-6, doi: 10.1109/INOCON50539.2020.9298408.
    [25] H. Hu, H. Zhang, Z. Zhao, B. Li and J. Zheng, "Adaptive single image dehazing using joint local-global illumination adjustment," in IEEE Transactions on Multimedia, vol. 22, no. 6, pp. 1485-1495, June 2020, doi: 10.1109/TMM.2019.2944260.
    [26] Li, Bo, et al. "Single image haze removal using content-adaptive dark channel and post enhancement." in IET Computer Vision 8.2 (2014): 131-140.
    [27] C. Li, J. Guo, R. Cong, Y. Pang and B. Wang, "Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior," in IEEE Transactions on Image Processing, vol. 25, no. 12, pp. 5664-5677, Dec. 2016, doi: 10.1109/TIP.2016.2612882.
    [28] H. Li, Q. Wu, K. N. Ngan, H. Li and a. F. Meng, "Single image dehazing via region adaptive two-shot network," in IEEE MultiMedia, vol. 28, no. 3, pp. 97-106, 1 July-Sept. 2021, doi: 10.1109/MMUL.2021.3052821.
    [29] J. Zhang, Y. Cao and Z. Wang, "Nighttime haze removal based on a new imaging model," in IEEE International Conference on Image Processing (ICIP), 2014, pp. 4557-4561, doi: 10.1109/ICIP.2014.7025924.
    [30] A. B. Yesilyurt, A. Erol, F. Kamisli and A. A. Alatan, "Single image noise level estimation using dark channel prior," 2019 IEEE International Conference on Image Processing (ICIP), 2019, pp. 2065-2069, doi: 10.1109/ICIP.2019.8803150.
    [31] A. Levin, D. Lischinski, and Y. Weiss, “A closed-form solution to natural image matting,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 2, pp. 228–242, Feb. 2008.
    [32] K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 6, pp. 1397–1409, Jun. 2013.
    [33] Z. Li, J. Zheng, Z. Zhu, W. Yao, and S. Wu, “Weighted guided image filtering,” IEEE Trans. Image Process., vol. 24, no. 1, pp. 120–129, Jan. 2015.
    [34] C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proc. IEEE Int. Conf. Comput. Vis., Jan. 1998, pp. 836–846.
    [35] S. Paris and F. Durand, “A fast approximation of the bilateral filter using a signal processing approach,” Dept. Comput. Sci. Artif. Intell. Lab., Massachusetts Inst. Technol., Cambridge, MA, USA, Tech. Rep. MIT-CSAIL-TR-2006-073, 2006.
    [36] G. Petschenigg, R. Szeliski, M. Agrawala, M. Cohen, H. Hoppe, and K. Toyama, “Digital photography with flash and no-flash image pairs,” ACM Trans. Graph., vol. 23, no. 3, pp. 664–672, Aug. 2004.
    [37] B. Jongmin and D. E. Jacobs, “Accelerating spatially varying Gaussian filters,” ACM Trans. Graph., vol. 29, no. 6, pp. 169-1–169-10, Dec. 2010.
    [38] P. Choudhury and K. J. Tumblin, “The trilateral filter for high contrast images and meshes,” in Proc. Eurograph. Symp. Rendering, 2003, pp. 186–196.
    [39] R. Fattal, “Edge-avoiding wavelets and their applications,” ACM Trans. Graph., vol. 28, no. 3, pp. 1–10, Aug. 2009.
    [40] D. Lischinski, Z. Farbman, M. Uyttendaele, and R. Szeliski, “Interactive local adjustment of tonal values,” ACM Trans. Graph., vol. 25, no. 3, pp. 646–653, 2006.
    [41] L. Xu, C. Lu, Y. Xu, and J. Jia, “Image smoothing via L0 gradient minimization,” ACM Trans. Graph., vol. 30, no. 6, pp. 174-1–174-12, 2011. [Online]. Available:http://doi.acm.org/10.1145/2024156.2024208
    [42] K. Zuiderveld., “Contrast limited adaptive histograph equalization.” In Graphic Gems IV. San Diego: Academic Press Professional, 1994. 474–485.
    [43] G. Cao, L. Huang, H. Tian, X. Huang, Y. Wang, R. Zhi, “Contrast enhancement of brightness-distorted images by improved adaptive gamma correction,” in Computers & Electrical Engineering, Volume 66, 2018, Pages 569-582, ISSN 0045-7906.
    [44] S. Huang, F. Cheng and Y. Chiu, “Efficient contrast enhancement using adaptive gamma correction with weighting distribution,” in IEEE Transactions on Image Processing, vol. 22, no. 3, pp. 1032-1041, March 2013, doi: 10.1109/TIP.2012.2226047.
    [45] S. Yelmanov and Y. Romanyshyn, "Image contrast enhancement in automatic mode by nonlinear stretching," in International Conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH), 2018, pp.104-108.
    [46] J.-S. Lin, P.-J. Liu, Y.-Y. Liao, and S.-C. Tai, “Level-base compounded logarithmic curve function for colour image enhancement,” IET Image Process., vol. 6, no. 7, pp. 943–958, Oct. 2012.
    [47] P. -J. Liu, S. -J. Horng, J. -S. Lin and T. Li, "Contrast in haze removal: configurable contrast enhancement model based on dark channel prior," in IEEE Transactions on Image Processing, vol. 28, no. 5, pp. 2212-2227, May 2019, doi: 10.1109/TIP.2018.2823424.
    [48] Y. Hang, L. Ding, G. Sharma, “HazeRD: An outdoor scene dataset and benchmark for single image dehazing,” Proc. IEEE Intl. Conf. Image Proc., pp. 3205–3209, 2017.
    [49] C. Ancuti, C. O. Ancuti and C. De Vleeschouwer, "D-HAZY: A dataset to evaluate quantitatively dehazing algorithms," 2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 2226-2230, doi: 10.1109/ICIP.2016.7532754.
    [50] C. O. Ancuti, C. Ancuti, R. Timofte, and C. D. Vleeschouwer, "I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images," arXiv:1804.05091v1, 2018
    [51] C. O.Ancuti, C. Ancuti, R. Timofte, and C. D. Vleeschouwer, “O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor Images,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 867-8678.
    [52] W. Zhou, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” in IEEE Transactions on Image Processing, vol. 13, issue 4, pp. 600-612, 2004.
    [53] P. -J. Liu, S. -J. Horng, J. -S. Lin and W.Zhou, "A restoration-oriented enhancement algorithm based on dark channel prior," submitted to IEEE Transactions on Image Processing, 2022.
    [54] H. Ullah et al., "Light-DehazeNet: a novel lightweight CNN architecture for single image dehazing," in IEEE Transactions on Image Processing, vol. 30, pp. 8968-8982, 2021, doi: 10.1109/TIP.2021.3116790.
    [55] W. Wang, A. Wang and C. Liu, "Variational single nighttime image haze removal with a gray haze-line prior," in IEEE Transactions on Image Processing, vol. 31, pp. 1349-1363, 2022, doi: 10.1109/TIP.2022.3141252.
    [56] J. Li, Y. Li, L. Zhuo, L. Kuang and T. Yu, "USID-Net: unsupervised single image dehazing network via disentangled representations," in IEEE Transactions on Multimedia, doi: 10.1109/TMM.2022.3163554.
    [57] D.L. Ruderman, T.W. Cronin, and C.C. Chiao, “Statistics of cone responses to natural images: Implications for visual coding,” J. Optical Soc. of Am. A, vol. 15, no. 8, pp. 2036-2045, 1998.
    [58] A. Wang, W. Wang, J. Liu and N. Gu, "AIPNet: image-to-image single image dehazing with atmospheric illumination prior," in IEEE Transactions on Image Processing, vol. 28, no. 1, pp. 381-393, Jan. 2019, doi: 10.1109/TIP.2018.2868567.
    [59] E. Land and J. McCann, “Lightness and retinex theory,” The Journal of the Optical Society of America A., vol. 61, no. 1, pp. 1–11, Jan. 1971.
    [60] G. Buchsbaum, “A spatial processor model for object colour perception,” J. Franklin Inst., vol. 310, no. 1, pp. 1-26, July 1980.
    [61] J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-based color constancy,” IEEE Trans. Image Processing, vol. 16, no. 9, pp. 2207-2214, Sept. 2007.
    [62] G. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in IS&T/SID Twelfth Color Imaging Conference, 2004, pp. 37–41.
    [63] N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, “Indoor segmentation and support inference from RGBD Images,” European Conference on Computer Vision, 2012.
    [64] M. Sulami, I. Geltzer, R. Fattal, and M. Werman, “Automatic recovery of the atmospheric light in hazy images,” in IEEE International Conference on Computational Photography (ICCP), 2014.
    [65] Sharma, Gaurav, Wencheng Wu, and Edul N. Dalal, "The CIEDE2000 color-difference formula: implementation notes, supplementary test sata, and mathematical observations". Color Research and Application 30, no. 1 (February 2005): 21–30.
    [66] H. Z. Nafchi, A. Shahkolaei, R. F. Moghaddam, M. Cheriet, “FSITM: a feature similarity index for tone-mapped images,” IEEE Signal Processing Letters, vol. 22, no. 8, pp. 1026-1029, 2015.
    [67] H. Yeganeh and Z. Wang, “Objective quality assessment of tone-mapped images,” Image Processing, IEEE Tans. On. Volume: 22, Issue: 2, Feb. 2013.
    [68] J.-M. Geusebroek and A. W. M. Smeulders, “A six-stimulus theory for stochastic texture.” International Journal of Computer Vision, vol. 62, no. 1-2, pp. 7-16, 2005.
    [69] N. Venkatanath, D. Praneeth, Bh. Maruthi Chandrasekhar, S. S. Channappayya and S. S. Medasani, "Blind image quality evaluation using perception based features," 2015 Twenty First National Conference on Communications (NCC), 2015, pp. 1-6, doi: 10.1109/NCC.2015.7084843.
    [70] A. Mittal, A. K. Moorthy and A. C. Bovik, "No-reference image quality assessment in the spatial domain," in IEEE Transactions on Image Processing, vol. 21, no. 12, pp. 4695-4708, Dec. 2012, doi: 10.1109/TIP.2012.2214050.
    [71] A. Mittal, R. Soundararajan and A. C. Bovik, "Making a “completely blind” image quality analyzer," in IEEE Signal Processing Letters, vol. 20, no. 3, pp. 209-212, March 2013, doi: 10.1109/LSP.2012.2227726.

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