Abstract
Guided refinement is a popular procedure of various image post-processing applications. It produces output image based on input and guided images. Input images are usually flawed estimates containing kinds of noises and outliers, which undermine the edge consistency between input and guided images. As improvements, they are refined into output images with similar intensities of input images and consistent edges of guided images. However, outliers are usually untraceable and simply treated as zero-mean noises, limiting the quality of such refinement. In this paper, we propose a general outlier detection method for guided refinement. We assume local linear relationship between output and guided images to express the expected edge consistency, based on which, the outlier likelihoods of input pixels are measured. The metric is termed as ALRe (anchored linear residual) since it is essentially the residual of local linear regression with an equality constraint exerted on the measured pixel. Valuable features of the ALRe are discussed. Its effectiveness is proven by applications and experiment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242 (2008)
Petschnigg, G., Szeliski, R., Agrawala, M., Cohen, M., Hoppe, H., Toyama, K.: Digital photography with flash and no-flash image pairs. ACM Trans. Graph. 23(3), 664–672 (2004)
Liu, W., Chen, X., Yang, J., Wu, Q.: Robust color guided depth map restoration. IEEE Trans. Image Process. 26(1), 315–327 (2016)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)
Hosni, A., Rhemann, C., Bleyer, M., Rother, C., Gelautz, M.: Fast cost-volume filtering for visual correspondence and beyond. IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 504–511 (2012)
Park, J., Kim, H., Tai, Y.W., Brown, M.S., Kweon, I.: High quality depth map upsampling for 3D-TOF cameras. In: International Conference on Computer Vision, pp. 1623–1630 (2011)
Yang, J., Ye, X., Li, K., Hou, C., Wang, Y.: Color-guided depth recovery from RGB-D data using an adaptive autoregressive model. IEEE Trans. Image Process. 23(8), 3443–3458 (2014)
Li, Z., Zheng, J., Zhu, Z., Yao, W., Wu, S.: Weighted guided image filtering. IEEE Trans. Image Process. 24(1), 120–129 (2014)
Dai, L., Yuan, M., Zhang, F., Zhang, X.: Fully connected guided image filtering. In: IEEE International Conference on Computer Vision, pp. 352–360 (2015)
Ochotorena, C.N., Yamashita, Y.: Anisotropic guided filtering. IEEE Trans. Image Process. 29, 1397–1412 (2019)
Farbman, Z., Fattal, R., Lischinski, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans. Graph. 27(3), 1–10 (2008)
He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013)
Berman, D., Avidan, S.: Non-local image dehazing. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1674–1682 (2016)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Ma, Z., He, K., Wei, Y., Sun, J., Wu, E.: Constant time weighted median filtering for stereo matching and beyond. In: IEEE International Conference on Computer Vision, pp. 49–56 (2013)
Fattal, R.: Dehazing using color-lines. ACM Trans. Graph. 34(1), 1–14 (2014)
Guo, X., Li, Y., Ma, J., Ling, H.: Mutually guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 42(3), 1283–1290 (2018)
Li, Y., Huang, J.B., Ahuja, N., Yang, M.H.: Joint image filtering with deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1909–1923 (2019)
Zhu, M., He, B., Liu, J., Yu, J.: Boosting dark channel dehazing via weighted local constant assumption. Sig. Process. 171, 107453 (2020)
Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: IEEE Conference on Computer Vision and Pattern Recognition, p. I (2003)
Scharstein, D., et al.: High-resolution stereo datasets with subpixel-accurate ground truth. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 31–42. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11752-2_3
Acknowledgements
This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFB1312800 and in part by the National Natural Science Foundation of China under Grant U1909206.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhu, M., Gao, Z., Yu, J., He, B., Liu, J. (2020). ALRe: Outlier Detection for Guided Refinement. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12352. Springer, Cham. https://doi.org/10.1007/978-3-030-58571-6_46
Download citation
DOI: https://doi.org/10.1007/978-3-030-58571-6_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58570-9
Online ISBN: 978-3-030-58571-6
eBook Packages: Computer ScienceComputer Science (R0)