Abstract
We present a new local window based image processing framework, which is particularly effective on edge-preserving and texture-removing. This seemingly contradictive effect is achieved by combining the traditional full window filtering strategy (FWF), which is good at removing noise, and the recently proposed side window filtering (SWF) strategy, which is good at preserving edges, so the new framework is called combined window filtering (CWF). By using window inherent variation method, we can easily distinguish the edges of structures from the texture. For the pixels on edges, SWF is used to preserve them and for the pixels on texture, FWF with multiple scales is used to remove them. This technique is surprisingly simple yet very effective in practice. We show that many traditional linear and nonlinear filters can be easily implemented under CWF framework. Extensive analysis and experiments show that implementing the CWF principle can significantly improve their edge-preserving and texture-removing capabilities and achieve state of the art performances in applications.
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References
Anaya, J., & Barbu, A. (2018). Renoir—A dataset for real low-light image noise reduction. Journal of Visual Communication and Image Representation, 51(2), 144–154.
Bi, S., Yu, Y., & Han, X. (2015). An L\(_1\) image transform for edge-preserving smoothing and scene-level intrinsic decomposition. ACM Transactions on Graphics, 34(4), 1–12.
Cho, H., Lee, H., Kang, H., & Lee, S. (2014). Bilateral texture filtering. ACM Transactions on Graphics, 33(4), 1–8.
Farbman, Z., Fattal, R., Lischinski, D., & Szeliski, R. (2008). Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Transactions on Graphics, 27(3), 67:1–67:10.
Fattal, R., Agrawala, M., & Rusinkiewicz, S. (2007). Multiscale shape and detail enhancement for multi-light image collection. ACM Transactions on Graphics, 26(3), 211–215.
Foi, A., Katkovnik, V., & Egiazarian, K. (2007). Pointwise shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color images. IEEE Transactions on Image Processing, 16(5), 1395–1411.
Gong, Y., & Sbalzarini, I. F. (2016). A natural-scene gradient distribution prior and its application in light-microscopy image processing. IEEE Journal of Selected Topics in Signal Processing, 10(1), 99–114.
Gong, Y., & Sbalzarini, I. F. (2017). Curvature filters efficiently reduce certain variational energies. IEEE Transactions on Image Processing, 26(4), 1786–1798.
He, K., Sun, J., & Tang, X. (2010). Guided image filtering. In: ECCV (pp. 1–14).
John, C. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698.
Kou, F., Chen, W., Wen, C., & Li, Z. (2015). Gradient domain guided image filtering. IEEE Transactions on Image Processing, 24(11), 4528–4539.
Liu, B., & Lu, X. (2018). Pointwise shape-adaptive texture filtering. IEEE International Conference on Multimedia and Expo, 1, 1–6.
Li, Z., Zheng, J., Zhu, Z., Yao, W., & Shiqian, W. (2015). Weighted guided image filtering. IEEE Transactions on Image Processing, 24(1), 120–129.
Michailovich, O. (2011). An iterative shrinkage approach to total-variation image restoration. IEEE Transactions on Image Processing, 20(5), 1281–1299.
Min, D., Choi, S., Lu, J., Ham, B., Sohn, K., & Do, M. (2014). Fast global image smoothing based on weighted least squares. IEEE Transactions on Image Processing, 23(12), 5638–5653.
Paris, S., & Durand, F. (2006). A fast approximation of the bilateral filter using a signal processing approach. In: ECCV (pp. 568–580).
Paris, S., Hasinoff, S. W., & Kautz, J. (2011). Local Laplacian filters: edge-aware image processing with a Laplacian pyramid. ACM Transactions on Graphics, 30(68), 1–68.
Rudin, L. I., Osher, S., & Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. Physica D, 60(1), 259–268.
Shen, X., Zhou, C., Xu, L., & Jia, J. (2017). Mutual-structure for joint filtering. IEEE International Journal of Computer Vision, 125, 19–33.
Sikora, T. (1995). Low complexity shape-adaptive dct for coding of arbitrarily shaped image segments. Signal Processing: Image Communication, 7(4), 381–395.
Tomasi, C., & Manduchi, R. (1998). Bilateral filtering for gray and color images. In ICCV (pp. 839–846).
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.
Xu, L., Lu, C., Xu, Y., & Jia, J. (2011). Image smoothing via l0 gradient minimization. ACM Transactions on Graphics, 30(6), 174:1–174:12.
Xu, L., Yan, Q., Xia, Y., & Jia, J. (2012). Structure extraction from texture via relative total variation. ACM Transactions on Graphics, 31(6), 139:1–139:10.
Yang, Q., Tan, K. H., & Ahuja, N.(2009). Real-time o(1) bilateral filtering. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 557–564).
Yin, H., Gong, Y., & Qiu, G. (2019a). Side window filtering. In CVPR (pp. 8758–8766).
Yin, H., Gong, Y., & Qiu, G. (2019b). Side window guided filtering. Signal Processing, 165, 315–330.
Zhang, Q., Shen, X., Xu, L., & Jia, J. (2014). Rolling guidance filter. In ECCV (pp. 815–830).
Zhu, F., Liang, Z., Jia, X., Zhang, L., & Yu, Y. (2019). A benchmark for edge-preserving image smoothing. IEEE Transactions on Image Processing, 28(7), 3556–3570.
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Yin, H., Gong, Y. & Qiu, G. Combined window filtering and its applications. Multidim Syst Sign Process 32, 313–333 (2021). https://doi.org/10.1007/s11045-020-00742-z
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DOI: https://doi.org/10.1007/s11045-020-00742-z