隨著人民安全意識覺醒，對於居家住宅、辦公大樓、商業店舖與都市公共區域（如車站、公園、道路、地下鐵）之安全監控需求提高，但犯罪及車禍之發生多於光線不佳、視線不良之夜間發生，因此夜間影像之品質攸關重大，為掌握證據、判斷真相之關鍵，惟習知影像處理多針對白天環境，鮮有就夜間人造光源、色散等影像失真進行補償，導致空有監控畫面，卻因光線不佳、色彩模糊，無法加以判讀，於事無補，而徒嘆負負；夜間影像之處理並無法直接循既有之白天環境處理模式，以除霧演算法之主要資訊為景深，現以Dark Channel Prior(DCP)自單一影像中估算景深最為熱門，白天中遠處對應至主要光源─天光，亮度最大，而較近處則亮度較低，透過背景光之量化推導影像深度，反觀夜間場景中光源並非天光，而多來自人造光源，遠處並無非至最大亮度反而漆黑一片，因此，DCP無法適用於夜間場景中，夜間影像除霧及品質增進之系統性研究尚付之闕如；本文針對改進夜間影像品質之殷切需求，提出結合大氣散射模型與人造光源資訊進行夜間影像除霧，並分割、追蹤影像序列中各移動物體進行亮度補償，使其還原後如同處於白天場景般清晰。
本文針對夜間場景之色散提出結合除霧及光源補償之影像強化演算法，可區分為白天背景影像除霧與夜間影像強化部份，首先透過DCP以白天背景影像之以除霧演算法移除色散造成之霧化效應，還原白天背景場景，並依照已知天光亮度推導各背景物體表面反射率，夜間影像強化部份，首先找出夜間背景影像固定、移動人造光源位置與亮度，並依照白天背景影像除霧所獲得之深度圖與各背景物體表面反射率資訊，推導夜間光源各波長能量衰減，再由夜間背景影像與待處理之夜間影像分割移動物體，根據白天影像除霧所取得之深度圖與人造光源亮度、位置與各波長能量衰減進一步計算移動物體表面反射率，最後將移動物體色彩還原、影像強化並放置於白天還原之場景。

Image surveillance is the major means of security monitoring. Image sequences obtained through surveillance cameras are vital sources for tracking criminal incidents and causes of accident, happening mostly at night due to lacking of light and obscurity of vision. The quality of the image plays a pivotal role in providing evidence and uncovering the truth. However, almost all image processing techniques focus on daylight environment, seldom on compensating artifacts rooted from artificial light source at night or light diffusion. The low-lighting environment and color obscurity often invalidate further identification from the surveillance video acquired.
The processing of images acquired at night cannot follow the paradigm of the daylight image processing. Take image dehazing for example, the removal of haze depends on the derivation of scene depth. Dark Channel Prior (DCP), using dark channel as a prior assumption, is often applied to derive scene depth from a single image. The farthest area, with the highest intensity of light, in an image corresponds to the major source of lighting – daylight, while the area closer with lower degree of light intensity, Therefore, the depth within the scene links with the amount of background light. The above observation does not hold at night. The source of light does not come from sun, rather artificial light source, e.g., street lamp or automobile headlight. The farthest area, often dark-pitch due to lack of any light source, does not have the highest light intensity. To the best of our knowledge, no research has been reported regarding the nighttime image dehazing and enhancement. In light of the demands of higher nighttime image quality, this paper proposes an image dehazing technique, incorporating the light diffusion model, artificial light source, and segmentation of moving objects within the image sequence, to restore the nighttime scene back to the daytime one.
The paper, employing the dehazing and image enhancement to remove the light diffusion in a nighttime image, is composed of daytime background dehazing and nighttime image enhancement. The scene depth is derived by applying DCP to the daytime background image, producing the corresponding depth map. The haze within the scene is removed by the dehazing algorithm to restore the daytime background. The reflectance of objects in the background can be further derived by taking the daylight intensity into consideration. The position and overall intensity of the artificial light sources can be determined through the nighttime background image first. The moving objects are then segmented from the image sequence. The reflectance of moving objects can be evaluated, given the depth map obtained from the daytime image, and position and overall intensity of the artificial light sources from the nighttime counterpart. Once the reflectance of moving objects are determined, the background and moving objects can be fused together given proper daytime lighting.

論文審定書..............................................................................................................i
中文摘要.................................................................................................................ii
英文摘要.................................................................................................................iii
第一章 簡介 ..........................................................................................................1
1.1 影像霧化 ..........................................................................................................2
1.2 影像色偏現象 ..................................................................................................4
1.3 研究總述 .......................................................................................................5
第二章 相關研究 ....................................................................................................6
2.1 影像強化技術 ................................................................................................6
2.2 白天影像除霧技術 .........................................................................................11
2. 3 水下影像除霧技術 ........................................................................................14
2.3.1 Dark Channel prior 取得物體至相機距離深度圖 d(x) ...................................16
2.3.2 移除拍攝場景之人造光源 L .........................................................................16
2.3.3 估測拍攝場景之水下深度 D .........................................................................17
2.3.4 修正水下影像深度涵蓋範圍 R ......................................................................18
2. 4 夜間影像強化技術 ........................................................................................19
第三章 研究方法 ....................................................................................................20
3.1 夜間影像霧化模型 ...........................................................................................22
3.2 夜間影像除霧處理流程 ....................................................................................25
3.2.1 白天影像除霧部份 ....................................................................................... 28
3.2.2 夜間影像除霧部份 ....................................................................................... 32
A. 偵測光源位置與亮度步驟 .................................................................................32
B. 辨識移動物體步驟 ............................................................................................34
C. 推導移動物體表面反射率步驟 ..........................................................................35
D. 影像場景還原與影像強化步驟 ..........................................................................36
第四章 實驗結果 ...................................................................................................37
第五章 結論與未來工作 .........................................................................................44
參考文獻 ................................................................................................................45

[1] 陳英璟，『結合除霧技術與波長能量補償之水下影像強化演算法』，國立中山大學資訊工程學系研究所(2011)。
[2] S. K. Nayar and S. G. Narasimhan, “Vision in bad weather,” Proc. of Seventh IEEE Int’l Conf. Computer Vision, vol. 2, pp. 820–827, 1999.
[3] Y. Y. Schechner, S. G. Narasimhan and S. K. Nayar, “Instant dehazing of images using polarization,” Proc. of IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 325–332, 2001.
[4] S. G. Narasimhan and S. K. Nayar, “Contrast restoration of weather degraded images,” Proc. of IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 6, pp. 713-724, 2003.
[5] J. P. Oakley and B. Hong, “Correction of simple contrast loss in color images,” Proc. of IEEE Trans. Image Processing, vol. 16, no. 2, pp. 511-522, 2007.
[6] R. Tan, “Visibility in bad weather from a single image,” Proc. of IEEE Conf. Computer Vision and Pattern Recognition, pp. 1-8, 2008.
[7] R. Fattal, “Single image dehazing,” ACM Transactions on Graphics, vol. 27, no. 3, pp. 1-9, 2008.
[8] K. He, J. Sun and X. Tang, “Single image haze removal using dark channel prior,” Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1956-1963, 2009.
[9] J. Tarel and N. Hauti, “Fast visibility restoration from a single color or gray level image,” Proc. of IEEE International Conference on Computer Vision (ICCV), pp. 2201-2208, 2009.
[10] X. J. Huang, Y. D. Lai and F. Chien, “Fast single image haze removal algorithm,” Journal of Computer applications, pp. 3028-3030, 2010.
[11] J. YU and D.P LI, “Physics-Based Fast Single Image Fog Removal,” Acta Automatica Sinica, 2011.
[12] K. Lebart, C. Smith E. Trucco, and D. M. Lane, “Automatic indexing of underwater survey video: algorithm and benchmarking method,” Proc. of IEEE J. Ocean. Eng., Vol. 28, No. 4, pp. 673 - 686, 2003.
[13] L. Chao and M. Wang, “Removal of Water Scattering,” Intl. conf. on Computer Engineering and Technology, Vol. 2, pp. 35-39, 2010.
[14] N. Carlevaris-Bianco, A. Mohan and R. M. Eustice, “Initial results in underwater single image dehazing,” Proc. of IEEE OCEANS, pp. 1-8, 2010.
[15] J.Y. Chiang and Y.C. Chen, “Underwater image enhancement by wavelength compensation and dehazing,” Proc. of IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1756–1769, 2012.
[16] M. C. W. van Rossum and Th. M. Nieuwenhuizen, “Multiple scattering of classical waves: microscopy, mesoscopy and diffusion,” Reviews of Modern Physics, Vol. 71, No. 1, pp. 313-371, 1999.
[17] S. Shwartz, E. Namer, Y. Y. and Schechner, “Blind haze separation,” Proc. of IEEE CVPR, Vol. 2, pp. 1984-1991, 2006.
[18] J. T. Houghton, “The Physics of Atmospheres,” Chapter 2, 2nd ed. Cambridge University Press, 2001.
[19] Luz Abril Torres-Méndez and Gregory Dudek, “Color Correction of Underwater Images for Aquatic Robot Inspection,” Lecture Notes in Computer Science, No. 3757, pp. 60-73, 2005.
[20] Y. Y. Schechner and N. Karpel, “Clear Underwater Vision,” Proc. of IEEE CVPR, Vol. 1, pp. 536-543, 2004.
[21] R. Kutka, “Reconstruction of Correct 3-D Perception on Screens Views at Different Distances,” Proc. of IEEE Transactions on Communications, Vol. 42, No. 1, pp. 29-33, 1994.
[22] A. Levin, D. Lischinski, and Y. Weiss, “A closed form solution to natural image matting,” Proc. of IEEE CVPR, Vol. 1, pp. 61-68, 2006.
[23] A. Yamasaki, H. Takauji, S. Kaneko, T. Kanade and H. Ohki, “Denighting: Enhancement of Nighttime Images for a Surveillance Camera,” Proc. of Conf. on ICPR 19th, 2008.
[24] D. T. Lin and K. Y. Huang, “Collaborative Pedestrian Tracking and Data Fusion With Multiple Cameras,” Proc. of IEEE Trans. on Information Forensics and Security, Vol. 6, No. 4, Dec. 2011.
[25] P. V. K. Borges, “Pedestrian Detection Based on Blob Motion Statistics,” Proc. of IEEE Trans. on Circuits and Systems for Video Technology, Vol. 23, No. 2, Feb. 2013.
[26] F. C. Cheng, S. C. Huang and Shanq-Jang Ruan, “Advanced background subtraction approach using Laplacian distribution model,” IEEE Int'l Conf. on Multimedia and Expo, July 19-23,2010.
[27] B. Yogameena, S. M. Mansoor Roomi, R. Jyothi Priya, S. Raju and V. Abhaikumar, “People/vehicle classification by recurrent motion of skeleton features,” Computer Vision IET, Vol. 6, Issue 5, Sept. 2012.
[28] A. R. Rivera, B. Ryu and O. Chae, “Content-Aware Dark Image Enhancement Through Channel Division,” Proc. of IEEE Transactions on Image Processing, Vol. 21, No. 9, Sept. 2012.