Image inpainting using reproducing kernel Hilbert space and Heaviside functions

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• 作者：Si Wang^a ; ^{uestcsiwang@163.com" class="auth_mail" title="E-mail the corresponding author} ; Weihong Guo^b ; ^{wxg49@case.edu" class="auth_mail" title="E-mail the corresponding author} ; Ting-Zhu Huang^a ; ^{tingzhuhuang@126.com" class="auth_mail" title="E-mail the corresponding author} ; Garvesh Raskutti^c ; ^{raskutti@stat.wisc.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Image inpainting ; Reproducing kernel Hilbert space ; Heaviside function ; Edge
• 刊名：Journal of Computational and Applied Mathematics
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：311
• 期：Complete
• 页码：551-564
• 全文大小：4253 K

文摘

Image inpainting, a technique of repairing damaged images, is an important topic in image processing. In this paper, we solve the problem from an intensity function estimation perspective. We assume the underlying image is defined on a continuous domain and belongs to a space spanned by a basis of a reproducing kernel Hilbert space and some variations of the Heaviside function. The reproducing kernel Hilbert space is used to model the smooth component of the image while Heaviside function variations are used to model the edges. The coefficients of the redundant basis are computed by the discrete intensity at undamaged domain. We test the proposed model through various images. Numerical experiments show the effectiveness of the proposed method, especially in recovering edges.