Multiframe demosaicing and super-resolution of color images

被引:232
作者
Farsiu, S [1 ]
Elad, M
Milanfar, P
机构
[1] Univ Calif Santa Cruz, Dept Elect Engn, Santa Cruz, CA 95064 USA
[2] Technion Israel Inst Technol, Dept Comp Sci, IL-32000 Haifa, Israel
基金
美国国家科学基金会;
关键词
color enhancement; demosaicing; image restoration; robust estimation; robust regularization; super resolution;
D O I
10.1109/TIP.2005.860336
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In the last two decades, two related categories of problems have been studied independently in image restoration literature: super-resolution and demosaicing. A closer look at these problems reveals the relation between them, and, as conventional color digital cameras suffer from both low-spatial resolution and color-filtering, it is reasonable to address them in a unified context. In this paper, we propose a fast and robust hybrid method of super-resolution and demosaicing, based on a maximum a posteriori estimation technique by minimizing a multiterm cost function. The L, norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image, removing outliers in the data and errors due to possibly inaccurate motion estimation. Bilateral regularization is used for spatially regularizing the luminance component, resulting in sharp edges and forcing interpolation along the edges and not across them. Simultaneously, Tikhonov regularization is used to smooth the chrominance components. Finally, an additional regularization term is used to force similar edge location and orientation in different color channels. We show that the minimization of the total cost function is relatively easy and fast. Experimental results on synthetic and real data sets confirm the effectiveness of our method.
引用
收藏
页码:141 / 159
页数:19
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