In this paper, we propose least-squares images(LS-images) as a basis for a novel edgepreserving image smoothing method. The LS-image requires the value of each pixel to be a convex linear combination of its neighbors, i.e., to have zero Laplacian, and to approximate the original image in a least-squares sense. The edge-preserving property inherits from the edge-aware weights for constructing the linear combination. Experimental results demonstrate that the proposed method achieves high quality results compared to previous state-of-theart works. We also show diverse applications of LSimages, such as detail manipulation, edge enhancement,and clip-art JPEG artifact removal.
Hui WangJunjie CaoXiuping LiuJianmin WangTongrang FanJianping Hu
In this paper, we propose a novel geometrical detail editing method for triangulated mesh models based on filtering robust differential edge coordinates. The introduced detail editing consists of not only feature-preserving denoising for removing scanner noises, but also interactive detail editing for weakening or enhancing some specific geometric details. Various detail editing results are obtained by reconstructing the mesh from new processed differential edge coordinates, which are filtered from the view of signal processing, in linear least square sense. Experimental results and comparisons with other methods demonstrate that our method is effective and robust.
WANG HuiCAO Jun-jieLIU Xiu-pingFAN Tong-rangWANG Jian-min
