Abstract
Guided filter can perform edge-preserving smoothing by utilizing the structures of a guidance image. However, it is difficult to obtain two images with different contents from the same scenario. Therefore, we focus on the case that the input image and the guidance image are identical. In this case, the direction of the gradient of the output image is the same as the guidance image. Based on this discovery, we change the regularization term of guided filter and develop a more general model which can generate a bank of guided filters. To take examples, we pick up three filters from this bank, where \(L_1\) guided filter and \(L_{0.5}\) guided filter are newly proposed filters. Mathematical and experimental analysis are performed to demonstrate that the new filters have totally different properties from the guided filter. \(L_1\) guided filter is very suitable for edge-preserving and texture-removing tasks, while \(L_{0.5}\) guided filter can do enhancement automatically. We applied them to a variety of image processing applications, including image smoothing, image denoising, edge detection, image detail enhancement and X-ray image enhancement. The experimental results reveals the effectiveness of the newly proposed filters and their state of the art performance. We also believe that more interesting filters can be developed from our model.
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
Anaya, J., Barbu, A.: Renoir - a dataset for real low-light image noise reduction. J. Vis. Commun. Image Rep. 51(2), 144–154 (2018)
Dai, L.: Interpreting and extending the guided filter via cyclic coordinate descent. arXiv, 1705.10552v1
Durand, F., Dorsey, J.: Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans. Graph. 21(3), 257–266 (2002)
Farbman, Z., Fattal, R., Lischinshi, D., Szeliski, R.: Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans. Graph. 27(3), 67:1–67:10 (2008)
Gong, Y., Sbalzarini, I.: Local weighted Gaussian curvature for image processing. In: IEEE International Conference on Image Processing (2013)
Gong, Y., Sbalzarini, I.: A natural-scene gradient distribution prior and its application in light-microscopy image processing. IEEE J. Sel. Top. Signal Process. 10(1), 99–114 (2016)
He, K., Sun, J., Tang, Z.: Guied image filtering. IEEE Trans. Pattern Anal. Mach. Learn. 35(6), 1397–1409 (2013)
John, C.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679–698 (1986)
Kou, F., Chen, W., Wen, C., Li, Z.: Gradient domain guided image filtering. IEEE Trans. Image Process. 24(11), 4528–4539 (2015)
Krishnan, D., Fergus, R.: Fast image deconvolution using hypre-laplacian priors. Neural Inf. Process. Syst. (2009)
Li, Z., Zheng, J., Rahardja, S.: Detail-enhanced exposure fusion. IEEE Trans. Image Process. 21(11), 4672–4676 (2012)
Li, Z., Zheng, J., Zhu, Z., Yao, W., Wu, S.: Weighted guided image filtering. IEEE Trans. Image Process. 24(1), 120–129 (2015)
Min, D., Choi, S., Lu, J., Ham, B., Sohn, K., Do, M.: Fast global image smoothing based on weighted least squares. IEEE Trans. Image Process. 23(12), 5638–5653 (2014)
Paris, S., Durand, F.: A fast approximation of the bilateral filter using a signal processing approach. In: ECCV, pp. 568–580 (2006)
Porikli, F.: Constant time o(1) bilateral filtering. In: CVPR (2008)
Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)
Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D Nonlinear Phenom. 60(1–4), 259–268 (1992)
Shen, X., Zhou, C., Xu, L., Jia, J.: Mutual-structure for joint filtering. IEEE Int. J. Comput. Vis. 125, 19–33 (2017)
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: ICCV, pp. 839–846 (1998)
Xu, L., Yan, Q., Xia, Y., Jia, J.: Structure extraction from texture via relative total variation. ACM Trans. Graph. 31(6), 139:1–139:10 (2012)
Yang, Y., Que, Y., Huang, S., Lin, P.: Multiple visual features measurement with gradient domain guided filtering for multisensor image fusion. IEEE Trans. Instrum. Meas. 66(4), 691–703 (2017)
Yin, W., Goldfarb, D., Osher, S.: Image cartoon-texture decomposition and feature selection using the total variation regularized l1 functional. In: International Workshop on Variational, Geometric and Level Set Methods in Computer Vision, pp. 73–84 (2005)
Zhang, C., Ge, L., Chen, Z., Qin, R., Li, M., Liu, W.: Guided filtering: toward edge-preserving for optical flow. IEEE Access 26958–26970 (2018)
Zhang, Q., Shen, X., Xu, L., Jia, J.: Rolling guidance filter. In: ECCV, pp. 815–830 (2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yin, H., Gong, Y., Qiu, G. (2022). Guided Filter Bank. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-80119-9_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80118-2
Online ISBN: 978-3-030-80119-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)