Abstract
Recent years have witnessed the emergence of image decomposition techniques which effectively separate an image into a piecewise smooth base layer and several residual detail layers. However, the intricacy of detail patterns in some cases may result in side-effects including remnant textures, wrongly-smoothed edges, and distorted appearance. We introduce a new way to construct an edge-preserving image decomposition with properties of detail smoothing, edge retention, and shape fitting. Our method has three main steps: suppressing high-contrast details via a windowed variation similarity measure, detecting salient edges to produce an edge-guided image, and fitting the original shape using a weighted least squares framework. Experimental results indicate that the proposed approach can appropriately smooth non-edge regions even when textures and structures are similar in scale. The effectiveness of our approach is demonstrated in the contexts of detail manipulation, HDR tone mapping, and image abstraction.
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Pan Shao received the B.S. degree in computer science from Shanghai Jiao Tong University in 2013. She is now working toward the M.S. degree in the Department of Computer Science and Engineering at Shanghai Jiao Tong University. Her research interests lie in image editing and face recognition.
Shouhong Ding received the B.S. and M.S. degrees from the School of Mathematical Sciences in Dalian University of Technology, China, in 2008 and 2011, respectively. He is now a Ph.D. candidate in the Department of Computer Science and Engineering, Shanghai Jiao Tong University, China. His current research interests include image/video editing, computer vision, computer graphics, and digital media technology.
Lizhuang Ma received his Ph.D. degree from Zhejiang University in 1991. He is now a full professor, Ph.D. tutor, and the head of the Digital Media Technology and Data Reconstruction Lab at the Department of Computer Science and Engineering, Shanghai Jiao Tong University since 2002. He is also the chairman of the Center of Information Science and Technology for Traditional Chinese Medicine in Shanghai Traditional Chinese Medicine University. His research interests include computer-aided geometric design, computer graphics, scientific data visualization, computer animation, digital media technology, and theory and applications for computer graphics, CAD/CAM.
Yunsheng Wu received his B.S. degree in computer science from Peking University, China. He is now a product director in Tencent Inc., China. He is responsible for QQ video, Pitu, QQ tornado, and Watermark Camera. His current research interest is face recognition.
Yongjian Wu received the M.S. degree from Computer School in Wuhan University, China, in 2008. He is now a senior researcher in Tencent Inc., China. His current research interests include computer vision, pattern recognition, and digital media technology.
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Shao, P., Ding, S., Ma, L. et al. Edge-preserving image decomposition via joint weighted least squares. Comp. Visual Media 1, 37–47 (2015). https://doi.org/10.1007/s41095-015-0006-4
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DOI: https://doi.org/10.1007/s41095-015-0006-4