1. Robotics Laboratory, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016 2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169 3. University of Chinese Academy of Sciences, Beijing 100049 4. School of Mechanical Engineering and Automation, Harbin Institute of Technology(Shenzhen), Shenzhen 518055
Abstract The hue information in color images is not easily susceptible to inference from specular reflection. Grounded on this fact, an effective algorithm of specular reflection separation with hue constraint is proposed. Images are clustered by its hue information. The fusion coefficient of diffuse reflection and specular reflection is obtained by calculating the pixel chromaticity and the illumination chromaticity. The bilateral filter is conducted on fusion coefficients to eliminate the noise impact. A diffuse reflection image is acquired according to the calculated specular reflection coefficients. The experimental results show that the proposed method removes specular reflection effectively with the details and edge information of the image retained and it achieves satisfactory visual effects in processing natural highlight pictures without ground truth.
Fund:National Natural Science Foundation of China(No.U2013210,61821005)
Corresponding Authors:
TIAN Jiandong, Ph.D., professor. His research interests include pattern recognition and robot vision.
About author:: ZHANG Zhen, Ph.D. candidate. His research interests include image processing and pattern recognition.REN Weihong, Ph.D., assistant profe-ssor. His research interests include image processing and pattern recognition.TANG Yandong, Ph.D., professor. His research interests include pattern recognition and robot vision.
[1] SALAH M B, MITICHE A, AYED I B. Multiregion Image Segmentation by Parametric Kernel Graph Cuts. IEEE Transactions on Image Processing, 2011, 20(2): 545-557. [2] FOSTER D H. Color Constancy. Vision Research, 2011, 51(7):674-700. [3] FELZENSZWALB P F, GIRSHICK R B, MCALLESTER D, et al. Object Detection with Discriminatively Trained Part-Based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645. [4] ROSS D A, LIM J, LIN R S, et al. Incremental Learning for Robust Visual Tracking. International Journal of Computer Vision, 2008, 77: 125-141. [5] SHAFER S A. Using Color to Separate Reflection Components. Co-lor Research and Application, 1985, 10(4): 210-218. [6] KLINKER G J, SHAFER S A, KANADE T. The Measurement of Highlights in Color Images. International Journal of Computer Vision, 1988, 2: 7-32. [7] TAN R T, NISHINO K, IKEUCHI K. Separating Reflection Components Based on Chromaticity and Noise Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(10): 1373-1379. [8] TAN P, LIN S, QUAN L, et al. Highlight Removal by Illumination-Constrained Inpainting // Proc of the 9th IEEE International Confe-rence on Computer Vision. Washington, USA: IEEE, 2003, I: 164-169. [9] LIN S, LI Y Z, KANG S B, et al. Separation and Depth Recovery from Image Sequences // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2002: 210-224. [10] WEISS Y. Deriving Intrinsic Images from Image Sequences // Proc of the 8th IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2001, II: 68-75. [11] MALLICK S P, ZICKLER T, BELHUMEUR P N, et al. Specula-rity Removal in Images and Videos: A PDE Approach // Proc of the 9th European Conference on Computer Vision. Berlin, Germany: Springer, 2006: 550-563. [12] YOON K J, CHOI Y, KWEON I S. Fast Separation of Reflection Components Using a Specularity-Invariant Image Representation // Proc of the International Conference on Image Processing. Wa-shington, USA: IEEE, 2006: 973-976. [13] SHEN H L, ZHANG H G, SHAO S J, et al. Chromaticity-Based Separation of Reflection Components in a Single Image. Pattern Recognition, 2008, 41(8): 2461-2469. [14] YANG J W, LIU L X, LI S Z. Separating Specular and Diffuse Reflection Components in the HSI Color Space // Proc of the IEEE International Conference on Computer Vision Workshops. Washington, USA: IEEE, 2013: 891-898. [15] YANG Q X, TANG J H, AHUJA N. Efficient and Robust Specular Highlight Removal. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(6): 1304-1311. [16] SHEN H L, ZHENG Z H. Real-Time Highlight Removal Using Intensity Ratio. Applied Optics, 2013, 52(19): 4483-4493. [17] KOIRALA P, HAUTA-KASARI M, PARKKINEN J. Highlight Removal from Single Image // Proc of the International Conference on Advanced Concepts for Intelligent Vision Systems. Berlin, Germany: Springer, 2009: 176-187. [18] HE Y, KHANNA N, BOUSHEY C J, et al. Specular Highlight Removal for Image-Based Dietary Assessment // Proc of the IEEE International Conference on Multimedia and Expo Workshops. Washington, USA: IEEE, 2012: 424-428. [19] KIM H, JIN H L, HADAP S, et al. Specular Reflection Separation Using Dark Channel Prior // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2013: 1460-1467. [20] AKASHI Y, OKATANI T. Separation of Reflection Components by Sparse Non-negative Matrix Factorization. Computer Vision and Image Understanding, 2016, 146: 77-85. [21] REN W H, TIAN J D, TANG Y D. Specular Reflection Separation with Color-Lines Constraint. IEEE Transactions on Image Proce-ssing, 2017, 26(5): 2327-2337. [22] GUO J, ZHOU Z J, WANG L M. Single Image Highlight Removal with a Sparse and Low-Rank Reflection Model // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2018: 282-298.
2021, Vol. 34 
