Abstract
The color correction algorithm is designed to eliminate color discrepancies between image pairs. Compared with the conventional algorithm, color correction for 3D stereoscopic images not only needs to achieve the color consistency of the resulting image and the reference image but also expected to ensure the structural consistency of the resulting image and the target image. For this problem, we propose a stereoscopic image color correction algorithm based on deep residual optimization. First, we get an initial result image by fusing a global color correction image and a dense matching image of the stereo image pair. Then, a residual image optimization scheme is used to improve the structural deformation and color inconsistency of the initial result caused by mismatching and fusion. By combining the target image with the optimized residual image, the structure and clarity of the target image can be retained to the maximum extent. In addition, we use the perceptual loss and per-pixel loss to improve the structural deformation and local color inconsistency while training the optimization network. Experimental results show the effectiveness of our method.
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References
Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. Int. J. Comput. Vision 74(1), 59–73 (2007)
Daniel, S.: http://vision.middlebury.edu/stereo/data/
Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2015)
Fecker, U., Barkowsky, M., Kaup, A.: Histogram-based prefiltering for luminance and chrominance compensation of multiview video. IEEE Trans. Circuits Syst. Video Technol. 18(9), 1258–1267 (2008)
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_43
Niu, Y., Zhang, H., Guo, W., Ji, R.: Image quality assessment for color correction based on color contrast similarity and color value difference. IEEE Trans. Circuits Syst. Video Technol. 28(4), 849–862 (2016)
Niu, Y., Zheng, X., Zhao, T., Chen, J.: Visually consistent color correctionfor stereoscopic images and videos. IEEE Trans. Circ. Syst. Video Technol. (2019)
Park, J., Tai, Y.W., Sinha, S.N., So Kweon, I.: Efficient and robust color consistency for community photo collections. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 430–438 (2016)
Pitie, F., Kokaram, A.C., Dahyot, R.: N-dimensional probability density function transfer and its application to color transfer. In: Tenth IEEE International Conference on Computer Vision (ICCV 2005), Volume 1, vol. 2, pp. 1434–1439. IEEE (2005)
Pitié, F., Kokaram, A.C., Dahyot, R.: Automated colour grading using colour distribution transfer. Comput. Vis. Image Underst. 107(1–2), 123–137 (2007)
Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graphics Appl. 21(5), 34–41 (2001)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Wang, Q., Yan, P., Yuan, Y., Li, X.: Robust color correction in stereo vision. In: 2011 18th IEEE International Conference on Image Processing, pp. 965–968. IEEE (2011)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P., et al.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Xiao, X., Ma, L.: Color transfer in correlated color space. In: Proceedings of the 2006 ACM International Conference on Virtual Reality Continuum and Its Applications, pp. 305–309. ACM (2006)
Xiao, X., Ma, L.: Gradient-preserving color transfer. In: Computer Graphics Forum, vol. 28, pp. 1879–1886. Wiley Online Library (2009)
Yan, Z., Li, X., Li, M., Zuo, W., Shan, S.: Shift-Net: image inpainting via deep feature rearrangement. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_1
Yao, C.H., Chang, C.Y., Chien, S.Y.: Example-based video color transfer. In: IEEE International Conference on Multimedia & Expo (2016)
Zhang, M., Georganas, N.D.: Fast color correction using principal regions mapping in different color spaces. Real-Time Imaging 10(1), 23–30 (2004)
Zheng, X., Niu, Y., Chen, J., Chen, Y.: Color correction for stereoscopic image based on matching and optimization. In: 2017 International Conference on 3D Immersion (IC3D), pp. 1–8. IEEE (2017)
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Fan, Y., Liu, P., Niu, Y. (2020). Deep Residual Optimization for Stereoscopic Image Color Correction. In: Shen, H., Sang, Y. (eds) Parallel Architectures, Algorithms and Programming. PAAP 2019. Communications in Computer and Information Science, vol 1163. Springer, Singapore. https://doi.org/10.1007/978-981-15-2767-8_14
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DOI: https://doi.org/10.1007/978-981-15-2767-8_14
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