Improved Stereo Image Dehazing Approach

  • Chin-Chen Chang
  • Chin-Chen Chang Department of NewMedia Art, Taipei National University of Arts, Taipei, Taiwan;
  • Der-Lor Way Department of NewMedia Art, Taipei National University of Arts, Taipei, Taiwan
  • Zen-Chung Shih Institute of Multimedia Engineering, National Chiao Tung University, Hsinchu, Taiwan

Abstract

Numerous image dehazing algorithms have been studied intensively. However, most dehazing algorithms operate on single images. These algorithms produce inconsistent results if they are used to dehaze stereo images iteratively. In this paper, we present a novel dehazing approach for stereo images based on cross bilateral filtering. In this approach, we simultaneously estimate scene depth and dehaze the stereo images. The proposed approach is based on the observation of depth cues in the stereo images. Depth cues are mainly used to avoid inconsistent results, and the cross bilateral filter is used to preserve shape details. The results demonstrate that the proposed approach can deliver superior results to those of previously published methods.

References

(1) P. Carr, R. Hartley, “Improved Single Image Dehazing Using Geometry”, Digital Image Computing: Techniques and Applications (DICTA), 103–110, 2009.

(2) H. Cho, H. Lee, H. Kang, S. Lee, “Bilateral Texture Filtering”, ACM Transactions on Graphics (TOG), 33(4), 1281–1288, 2014.

(3) R. Fattal, “Single Image Dehazing”, ACM Transactions on Graphics (TOG), 27(3), 7–23, 2008.

(4) R. Fattal, “Dehazing Using Color-Lines”, ACM Transactions on Graphics (TOG), 34(1), 1–14, 2014.

(5) K. Gibson, T. Nguyen, “Fast Single Image Fog Removal Using the Adaptive Wiener Filter”, Proceedings of the International Conference on Image Processing (ICIP), 714–718, 2013.

(6) R.I. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision, Cambridge: Cambridge University Press, 2004.

(7) K. He, J. Sun, X. Tang, “Single Image Haze Removal Using Dark Channel Prior”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 33(12), 2341–2353, 2011.

(8) J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, D. Lischinski, “Deep Photo: Model-Based Photograph Enhancement and Viewing”, ACM SIGGRAPH Asia, 2008.

(9) Z. Li, P. Tan, R.T. Tan, D. Zou, “Simultaneous Video Defogging and Stereo Reconstruction”, Computer Vision and Pattern Recognition (CVPR), 4988–4997, 2015.

(10) G. Meng, Y. Wang, J. Duan, X. Xiang, C. Pan, “Efficient Image Dehazing with Boundary Constraint and Contextual Regularization”, International Conference on Computer Vision (ICCV), 617–624, 2013.

(11) W.E.K. Middleton, Vision Through the Atmosphere, Toronto: University of Toronto Press, 1952.

(12) Y.H. Shiau, P.Y. Chen, H.Y. Yang, C.H. Chen, S.S. Wang, “Weighted Haze Removal Method with Halo Prevention”, Journal of Visual Communication and Image Representation, 25(2), 445–453, 2014.

(13) R.T. Tan, “Visibility in Bad Weather from A Single Image”, Computer Vision and Pattern Recognition (CVPR), 1–8, 2008.

(14) J.P. Tarel, H. Hautiere, “Fast Visibility Restoration from a Single Color or Gray Level Image”, International Conference on Computer Vision (ICCV), 2201–2208, 2009.

(15) J.P. Tarel, H. Hautiere, “Improved Visibility of Road Scene Images under Heterogeneous Fog”, Proceedings of IEEE Intelligent Vehicle Symposium (IV), 478–485. 2010.

(16) C. Tomasi, R. Manduchi, “Bilateral Filtering For Gray and Color Images”, International Conference on Computer Vision (ICCV), 839–846, 1998.

(17) J.B. Wang, N. He, K. Lu, “A New Single Image Dehazing Method with MSRCR Algorithm”, Proceedings of the International Conference on Internet Multimedia Computing and Service (ICIMCS), 2015.

(18) Q.X. Yang, “A Non-Local Cost Aggregation Method For Stereo Matching”, Computer Vision and Pattern Recognition (CVPR), 1402–1409, 2012.

(19) Q.X. Yang, N. Ahuja, K.H. Tan, “Constant Time Median and Bilateral Filtering”, International Journal of Computer Vision, 112(3), 307–318, 2015.

(20) Y.C. Wu, C.C. Chang, D.L. Way, Z.C. Shih, “A Novel Dehazing Approach for Stereo Images,” in Proceedings of the International Workshop on Advanced Image Technology 2017 (IWAIT 2017), Penang, Malaysia, January 2017.

(21) Z. Zhang, “A Flexible New Technique for Camera Calibration”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 22(11), 1330–1334, 2000.

Published
2019-01-01
How to Cite
Chang, C.-C., Chang, C.-C., Way, D.-L., & Shih, Z.-C. (2019). Improved Stereo Image Dehazing Approach. European Journal of Applied Sciences, 6(6), 01. https://doi.org/10.14738/aivp.66.5497