A Convex Regularizer for Reducing Color Artifact in Color Image Recovery
A Convex Regularizer for Reducing Color Artifact in Color Image Recovery
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DOI：
10.1109/cvpr.2013.232
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
Shunsuke Ono;I. Yamada
中科院分区：
文献类型：
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作者：
Shunsuke Ono;I. Yamada
提出了一种新的用于彩色图像恢复的凸正则化子，称为局部颜色核范数(LCNN)。LCNN旨在促进自然彩色图像固有的特性--其中它们的局部颜色分布通常表现出强烈的线性--因此有望有效地减少彩色伪影。此外，LCNN的本质允许我们将其合并到各种类型的彩色图像恢复公式中，相关的凸优化问题可以使用近邻分裂技术来解决。最后用数值算例说明了LCNN的应用。
We propose a new convex regularizer, named the local color nuclear norm (LCNN), for color image recovery. The LCNN is designed to promote a property inherent in natural color images - in which their local color distributions often exhibit strong linearity - and is thus expected to reduce color artifact effectively. In addition, the very nature of LCNN allows us to incorporate it into various types of color image recovery formulations, with the associated convex optimization problems solvable using proximal splitting techniques. Applications of LCNN are demonstrated with illustrative numerical examples.