本論文有兩項主要貢獻:首先提出一個混合式高效能除霧系統修復受霧影響的影像，我們以單張影像與光學模型為基礎精準定位環境光的確切位置以減少後續可能發生的退色問題。接著，我們提出混合式權重的概念，根據不同特性的影像區域給予不同的強化程度，以期能在強化能見度的同時仍保留相當的自然程度。在影片應用中，本演算法亦可藉由自動更新機制，減少影片中幀與幀連續處理下可能產生的閃爍效應。實驗結果顯示，本演算法除了可以重現更自然與細節之影像，亦可有效地重建高度霧化的區域。除此之外，高效能的運算複雜度使得本演算法適用於各種實際場合。
其二，鑒於以傳統光學模型為基礎之除霧演算法常常無法有效強化暗部細節，同時於天空亦常有過分增強之效應，我們亦提出了高效能對比限制直方圖等化除霧系統，輔以參數之自適應調整及轉換至不同的色域空間，整合傳統以直方圖為基礎之技術與He[15] 等人所提出的暗通道先驗方法，強化暗部細節同時保留影像的自然程度與由光學模型產生的色彩資訊。實驗結果顯示，本演算法不論在影像中暗部或亮部皆能有效強化紋理細節與回復能見度，同時亦不會有色退或色偏之問題。另外，由於我們所提出的高效能對比限制直方圖等化技術，其運算時間相較傳統之對比限制直方圖等化技術可被大幅縮短，因此仍相當適合多樣實際的視覺應用場合。

Image quality degradation is often introduced by capturing in poor weather conditions such as fog or haze. To overcome this problem, the conventional approaches focus mainly on the enhancement of the overall image contrast. However, because of the unspecified light-source distribution or unsuitable mathematical constraints of the cost functions, quality results are often difficult to achieve. In this thesis, a fusion-based transmission estimation method is introduced to adaptively combine two different transmission models. Specifically, the new fusion weighting scheme and the atmospheric light computed from the Gaussian-based dark channel method improves the estimation of the locations of the light sources. To reduce the flickering effect introduced during the process of frame-based dehazing, a flicker-free module is formulated to alleviate the impacts. The system assessments show this approach is capable of superior defogging and dehazing performance, compared to the state-of-the-art methods, both quantitatively and qualitatively.
However, due to the inner constraints of the optical-based defogging, the local image details are usually sacrificed and therefore degrade the practicability. In this thesis, we also proposed another solution to solve this issue. The traditional image enhancement method, contrast limited adaptive histogram equalization (CLAHE), is further exploited by reducing its computational complexity, and then combined with the optical-based defogging method to enhance the image detail while preserving the color fidelity. To solve with the over bright and low contrast issue resulted from the unsuitable block size, an adaptive refinement module based two brightness channels is also proposed. The quantitative and qualitative system assessment shows that the proposed approach achieves a superior defogging performance, and maintains the image naturalness effectively compared to the state-of-art methods, making it the best candidate for various applications.

[1] X. Wang, M. Wang, and W. Li, "Scene-Specific Pedestrian Detection for Static Video Surveillance", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 2, pp. 361-374, 2014.
[2] W. S. Zheng, S. Gong, and T. Xiang, "Quantifying and Transferring Contextual Information in Object Detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 4, pp. 762-777, 2012.
[3] A. Almagambetov, S. Velipasalar and M. Casares, "Robust and Computationally Lightweight Autonomous Tracking of Vehicle Taillights and Signal Detection by Embedded Smart Cameras", IEEE Trans. Ind. Electron., vol. 62, no. 6, pp. 3732-3741, 2015.
[4] M. Cheon, W. Lee, C. Yoon and M. Park, "Vision-Based Vehicle Detection System With Consideration of the Detecting Location", IEEE Trans. Intell. Transport. Syst., vol. 13, no. 3, pp. 1243-1252, 2012.
[5] A. Jazayeri, H. Cai, J. Zheng and M. Tuceryan, "Vehicle Detection and Tracking in Car Video Based on Motion Model", IEEE Trans. Intell. Transport. Syst., vol. 12, no. 2, pp. 583-595, 2011.
[6] S. Narasimhan and S. Nayar, "Contrast restoration of weather degraded images", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 6, pp. 713-724, 2003.
[7] S. K. Nayar and S. G. Narasimhan, "Vision in bad weather," in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), vol. 2. Sep. 1999, pp. 820–827.
[8] Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar, "Instant dehazing of images using polarization," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2001, pp. I-325–I-332.
[9] S. Shwartz, E. Namer, and Y. Y. Schechner, "Blind haze separation," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), vol. 2. 2006, pp. 1984–1991.
[10] S. G. Narasimhan and S. K. Nayar, "Interactive (de) weathering of an image using physical models," in Proc. IEEE Workshop Color Photometric Methods Comput. Vis., vol. 6. France, 2003, p. 1.
[11] N. Hautiere, J. Tarel, and D. Aubert, "Towards fog-free in-vehicle vision systems through contrast restoration," Proc. IEEE Conf. Computer Vision, Pattern Recognition, pp. 1-8, June 2007.
[12] H. Koschmider, "Theorie der horizontalen sichtweite," in Proc. Beiträgezur Phys. der Freien Atmos., 1924, pp. 171–181.
[13] J.-P. Tarel and N. Hautiere, "Fast visibility restoration from a single color or gray level image," in Proc. IEEE 12th Int. Conf. Comput. Vis. (ICCV), Sep./Oct. 2009, pp. 2201–2208.
[14] R. Tan. Visibility in bad weather from a single image. In IEEE Conference on Computer Vision and Pattern Recogntion (CVPR’08), pages 1–8, 2008.
[15] K. He, J. Sun, and X. Tang, "Single image haze removal using dark channel prior," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 12, pp. 2341–2353, Dec. 2011
[16] A. Levin, D. Lischinski, and Y. Weiss, "A Closed Form Solution to Natural Image Matting, " Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 61-68, 2006.
[17] W. J. Li, B. Gu, J. T. Huang , S. Y. Wang, and M. H. Wang, "Single image visibility enhancement in gradient domain," IET Image Process., vol. 6, no. 5, pp. 589–595, Jul. 2012
[18] Fattal, R., Lischinski, D., Werman, M.: "Gradient domain high dynamic range compression", ACM Trans. Graph., 2002, 21, (3), pp. 249 – 256
[19] Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: "Numerical recipes in C.: ‘The art of scientific computing" (Cambridge University Press, Cambridge, 1988, 2nd edn.)
[20] G. Meng, Y. Wang, J. Duan, S. Xiang, C. Pan. "Efficient image dehazing with boundary constraint and contextual regularization." Proceedings of the 2013 IEEE International Conference on Computer Vision (ICCV), 2013, December, pp. 617–624.
[21] Y. Lai, Y. Chen, C. Chiou, and C. Hsu, "Single-Image Dehazing via Optimal Transmission Map Under Scene Priors", IEEE Trans. Circuits Syst. Video Technol., vol. 25, no. 1, pp. 1-14, 2015.
[22] B. Xie, F. Guo, and Z. Cai, “Improved single image dehazing using dark channel prior and multi-scale retinex,” in Proc. Int. Conf. Intell. Syst. Design Eng. Appl., Oct. 2010, pp. 848–851.
[23] J. Kim, W. Jang, J. Sim and C. Kim, "Optimized contrast enhancement for real-time image and video dehazing", Journal of Visual Communication and Image Representation, vol. 24, no. 3, pp. 410-425, 2013.
[24] I. Yoon, S. Kim, D. Kim, M. Hayes, and J. Paik, ”Adaptive defogging with color correction in the HSV color space for consumer surveillance system,” IEEE Trans. Consumer Electronics, vol. 58, no. 1, pp. 111-116, February 2012.
[25] L. K. Choi, J. You, and A. C. Bovik. "Referenceless prediction of perceptual fog density and perceptual image defogging, " IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3888-3901, November 2015.
[26] K. He, J. Sun and X. Tang, "Guided Image Filtering", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1397-1409, 2013.
[27] M. Hofmann, P. Tiefenbacher, and G. Rigoll, "Background segmentation with feedback: The pixel-based adaptive segmenter," in Proc. IEEE Comput. Soc. Conf. CVPRW, Jun. 2012, pp. 38–43.
[28] P.-L. St-Charles, G.-A. Bilodeau, and R. Bergevin, "A Self-Adjusting Approach to Change Detection Based on Background Word Consensus" in IEEE Winter Conference on Applications of Computer Vision (WACV), Jan. 6-9, 2015.
[29] O. Barnich and M. Van Droogenbroeck, "ViBe: A universal background subtraction algorithm for video sequences," IEEE Trans. Image Process., vol. 20, no. 6, pp. 1709–1724, Jun. 2011.
[30] Z. Wang and Q. Li, "Information content weighting for perceptual image quality assessment," IEEE Trans. Image Process., vol. 20, no. 5, pp. 1185–1198, May 2011
[31] Q. Zhu, J. Mai and L. Shao, "A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior," Image Processing, IEEE Transactions on, On page(s): 3522 - 3533 Volume: 24, Issue: 11, Nov. 2015.
[32] N. Hautière, J.-P. Tarel, D. Aubert, and E. Dumont, "Blind contrast enhancement assessment by gradient rationing at visible edges," Image Anal. Stereol., vol. 27, no. 2, pp. 87–95, 2008.
[33] A. K. Moorthy and A. C. Bovik, "Blind image quality assessment: From natural scene statistics to perceptual quality," IEEE Trans. Image Process., vol. 20, no. 12, pp. 3350–3364, Dec. 2011.
[34] A. K. Moorthy and A. C. Bovik, "A Two-step Framework for Constructing Blind Image Quality Indices," IEEE Signal Processing Letters, pp. 587-599, vol. 17, no. 5, May 2010.
[35] N. Draper and H. Smith, Applied Regression Analysis, second ed. John Wiley, 1981.
[36] T. Hastie, R. Tibshirani, and J.H. Friedman, The Elements of Statistical Learning. Springer, 2003.
[37] Y. F. Liu, J. M. Guo, B. S. Lai, and J. D. Lee, "High efficient contrast enhancement using parametric approximation," in Proc. IEEE ICASSP, pp. 2444-2448, 26-31 May 2013.
[38] Gonzalez, R. C. and Woods, R. E. Digital Image Processing, Addison-Wesley, Reading, MA.
[39] J. A. Stark, "Adaptive image contrast enhancement using generalizations of histogram equalization," IEEE Trans. Image Processing, vol. 9, no. 5, pp. 889-896, 2000.
[40] S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. M. ter Haar Romeny, J. B. Zimmerman, and K. Zuiderveld, “Adaptive histogram equalization and its variations,” Computer Vision, Graphics, and Image Processing, vol. 39, no. 3, pp. 355-368, 1987.
[41] K. Zuiderveld, “Contrast limited adaptive histogram equalization,” Graphics Gems IV, Academic Press, 1994.
[42] S. M. Pizer, R. E. Johnston, J. P. Ericksen, B. C. Yankaskas, and K. E. Muller, “Contrast-limited adaptive histogram equalization: speed and effectiveness,” in Proc. the First Conference on Visualization in Biomedical Computing, pp. 337-345, 1990.
[43] P. Viola and M. J. Jones, "Robust real-time object detection," Second International Workshop on Statistical and Computational Theories of Vision, 2001.
[44] Hoo, S. C., Ibrahim. H, "Evaluations on Different Window Size Towards the Performance of Block Overlapped Histogram Equalization Method" International Conference on Intelligent Systems, Modelling and Simulation, pp. 249-254, 2014
[45] B. S. Min, D. K. Lim, S. J. Kim, and J. H. Lee, “A novel method of determining parameter of CLAHE based on image entropy,” International Journal of Software Engineering and its Applications, vol. 7, no. 5, pp. 113-120, 2013.
[46] J.C. Yu, “Speed-Up Parametric-Oriented and Contrast Limited Adaptive Histogram Equalization,” M.S. thesis, Dept. Electric. Eng., Taiwan Tech., Taipei, Taiwan, 2015.
[47] S. Wang, J. Zheng, H. Hu, and B. Li, “Naturalness preserved enhancement algorithm for non-uniform illumination images,” IEEE Trans. on Image Processing, vol. 22, no. 9, pp. 3538 – 3548, 2013.
[48] S. Chen and A. Beghdadi, “Natural rendering of color image based on retinex,” in Proc. IEEE Int. Conf. Image Process., Nov. 2009, pp. 1813–1816.