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,...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.展开更多
基金supported by National Natural Science Foundation of China (Nos. 61402300, 61373160, 61363048, 61173102, 61370143, and 61202261)Natural Science Foundation of Hebei Province (No. F2014210127)+2 种基金the Funded Projects for Introduction of Overseas Scholars of Hebei ProvinceFunds for Excellent Young Scholar of Shijiazhuang Tiedao UniversityScientific and Technological Development Plan of Jilin Province (No. 20130522113JH)
文摘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.
