Abstract
As a local filter, the guided image filtering (GIF) suffers from halo artifacts. To address this issue, a novel weighted aggregating strategy is proposed in this paper. By introducing the weighted aggregation to GIF, the proposed method called WAGIF can achieve results with sharp edges and avoid/reduce halo artifacts. More specifically, compared to the weighted guided image filtering and the gradient domain guided image filtering, the proposed method can achieve both fine and coarse smoothing results in the flat areas while preserving edges. In addition, the complexity of the proposed approach is O(N) for an image with N pixels. It is demonstrated that the GIF with weighted aggregation performs well in the fields of computational photography and image processing, including single image detail enhancement, tone mapping of high-dynamic-range images, single image haze removal, etc.
Similar content being viewed by others
References
Ali, S., Daul, C., Galbrun, E., Guillemin, F., Blondel, W.: Anisotropic motion estimation on edge preserving riesz wavelets for robust video mosaicing. Pattern Recognit. 51, 425–442 (2016). https://doi.org/10.1016/j.patcog.2015.09.021
Choi, L.K., You, J., Bovik, A.C.: Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans. Image Process. 24(11), 3888–3901 (2015). https://doi.org/10.1109/TIP.2015.2456502
Crow, F.C.: Summed-area tables for texture mapping. SIGGRAPH Comput. Graph. 18(3), 207–212 (1984). https://doi.org/10.1145/964965.808600
Durand, F., Dorsey, J.: Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans. Graph. 21(3), 257–266 (2002). https://doi.org/10.1145/566654.566574
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). https://doi.org/10.1145/1360612.1360666
Fattal, R., Agrawala, M., Rusinkiewicz, S.: Multiscale shape and detail enhancement from multi-light image collections. ACM Trans. Graph. (2007). https://doi.org/10.1145/1276377.1276441
Fattal, R., Lischinski, D., Werman, M.: Gradient domain high dynamic range compression. ACM Trans. Graph. 21(3), 249–256 (2002). https://doi.org/10.1145/566654.566573
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)
He, K., Sun, J., Tang, X.: Guided image filtering. IEEE Trans. Pattern Anal. Mach. Intell. 35(6), 1397–1409 (2013). https://doi.org/10.1109/TPAMI.2012.213
Jiang, X., Yao, H., Liu, D.: Nighttime image enhancement based on image decomposition. Signal Image Video Process. 13(1), 189–197 (2019). https://doi.org/10.1007/s11760-018-1345-2
Kim, B.K., Park, R.H., Chang, S.: Tone mapping with contrast preservation and lightness correction in high dynamic range imaging. Signal Image Video Process. 10(8), 1425–1432 (2016). https://doi.org/10.1007/s11760-016-0942-1
Kou, F., Chen, W., Wen, C., Li, Z.: Gradient domain guided image filtering. IEEE Trans. Image Process. 24(11), 4528–4539 (2015). https://doi.org/10.1109/TIP.2015.2468183
Li, X., Yan, Q., Yang, X., Jia, J.: Structure extraction from texture via relative total variation. ACM Trans. Graph. 31(6), 1–10 (2012)
Li, Y., Sharan, L., Adelson, E.H.: Compressing and companding high dynamic range images with subband architectures. ACM Trans. Graph. 24(3), 836–844 (2005). https://doi.org/10.1145/1073204.1073271
Li, Z., Zheng, J., Zhu, Z., Yao, W., Wu, S.: Weighted guided image filtering. IEEE Trans. Image Process. 24(1), 120–129 (2015). https://doi.org/10.1109/TIP.2014.2371234
Michailovich, O.V.: An iterative shrinkage approach to total-variation image restoration. IEEE Trans. Image Process. 20(5), 1281–1299 (2011). https://doi.org/10.1109/TIP.2010.2090532
Min, D., Choi, S., Lu, J., Ham, B., Sohn, K., Do, M.N.: Fast global image smoothing based on weighted least squares. IEEE Trans. Image Process. 23(12), 5638–5653 (2014). https://doi.org/10.1109/TIP.2014.2366600
Mun, J., Jang, Y., Kim, J.: Propagated guided image filtering for edge-preserving smoothing. Signal Image Video Process. 12(6), 1165–1172 (2018). https://doi.org/10.1007/s11760-018-1268-y
Oliveira, M.M.: Domain transform for edge-aware image and video processing. ACM Trans. Graph. 30(4), 1–12 (2011). https://doi.org/10.1145/2010324.1964964
Paras, J., Vipin, T.: An adaptive edge-preserving image denoising technique using patch-based weighted-SVD filtering in wavelet domain. Multimed. Tools Appl. 76(2), 1659–1679 (2017). https://doi.org/10.1007/s11042-015-3154-8
Pham, C.C., Ha, S.V.U., Jeon, J.W.: Adaptive guided image filtering for sharpness enhancement and noise reduction. In: Pacific Rim Conference on Advances in Image and Video Technology, pp. 323–334 (2011)
Porikli, F.: Constant time O(1) bilateral filtering. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008). https://doi.org/10.1109/CVPR.2008.4587843
Ren, W., Liu, S., Ma, L., Xu, Q., Xu, X., Cao, X., Du, J., Yang, M.: Low-light image enhancement via a deep hybrid network. IEEE Trans. Image Process. 28(9), 4364–4375 (2019). https://doi.org/10.1109/TIP.2019.2910412
Ren, W., Ma, L., Zhang, J., Pan, J., Cao, X., Liu, W., Yang, M.: Gated fusion network for single image dehazing. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3253–3261 (2018). https://doi.org/10.1109/CVPR.2018.00343
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: IEEE International Conference on Computer Vision, pp. 839–846 (1998). https://doi.org/10.1109/ICCV.1998.710815
Xu, L., Lu, C., Xu, Y., Jia, J.: Image smoothing via \(L_0\) gradient minimization. ACM Trans. Graph. 30(6), 1–12 (2011). https://doi.org/10.1145/2070781.2024208
You, X., Du, L., Cheung, Y., Chen, Q.: A blind watermarking scheme using new nontensor product wavelet filter banks. IEEE Trans. Image Process. 19(12), 3271–3284 (2010). https://doi.org/10.1109/TIP.2010.2055570
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grants 61775172 and 61371190. The authors wish to acknowledge the anonymous reviewers’ insightful and inspirational comments that have greatly helped to improve the technical contents and readability of this paper.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Chen, B., Wu, S. Weighted aggregation for guided image filtering. SIViP 14, 491–498 (2020). https://doi.org/10.1007/s11760-019-01579-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-019-01579-1