Result Of STD
We compared our method with some existing methods, included the total variation L1 (TV-L1), weight least square (WLS), L0 smoothing (L0), relative total variation (RTV), rolling guided filtering (RGF), structure gradient and texture decorrelating regularization (SGTD), scale-aware structure-preserving texture filtering (STF), joint convolutional analysis and synthesis sparse representation (JCAS), image smoothing via unsupervised learning (IUL), side window filtering (SWF).
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 S. Gu, D. Meng, W. Zuo, and L. Zhang, “Joint convolutional analysis and synthesis sparse representation for single image layer separation,” in Proc. IEEE Int. Conf. Comput. Vis., 2017, pp. 1717-1725
 Q. Fan, J. Yang, D. Wipe, B. Chen, and X. Tong, “Image smoothing via unsupervised learning,” ACM Trans. Graph., vol. 37, no. 6, pp. 259:1- 259:14, 2018
 H. Yin, Y. Gong, and G. Qiu, “Side window filtering,” to appear in IEEE Conf. Comput. Vis. Pattern Recog., 2019
Fei Zhou: email@example.com
Qun Chen: firstname.lastname@example.org
Results from different combinations of functional spaces
Results of texture removal