POCS-Based Restoration of Bayer-Sampled Image Sequences
POCS-Based Restoration of Bayer-Sampled Image Sequences
复制标题
基于 POCS 的拜耳采样图像序列恢复
DOI：
10.1109/icassp.2007.366017
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Y. Altunbasak
中科院分区：
文献类型：
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作者：
Murat Gevrekci;B. Gunturk;Y. Altunbasak
由于光学/传感器模糊和传感器位置密度，数字图像的空间分辨率受到限制。在单芯片数码相机中，分辨率进一步下降，因为此类设备使用滤色器阵列仅捕获像素位置处的一个光谱分量。估计每个像素位置处缺失的两个颜色值的过程称为去马赛克。去马赛克方法通常利用颜色通道之间的相关性。当存在多个图像时，不仅可以更好地估计缺失的颜色值，还可以进一步提高空间分辨率（使用超分辨率重建）。之前，我们提出了一种基于凸集投影（POCS）技术的去马赛克算法。在本文中，我们改进了该算法的结果，添加了基于空间强度邻域的新约束集。我们将该算法扩展到图像序列，以进行多帧去马赛克和超分辨率。
Spatial resolution of digital images are limited due to optical/sensor blurring and sensor site density. In single-chip digital cameras, the resolution is further degraded because such devices use a color filter array to capture only one spectral component at a pixel location. The process of estimating the missing two color values at each pixel location is known as demosaicking. Demosaicking methods usually exploit the correlation among color channels. When there are multiple images, it is possible not only to have better estimates of the missing color values but also to improve the spatial resolution further (using super-resolution reconstruction). Previously, we have proposed a demosaicking algorithm based on the projection onto convex sets (POCS) technique. In this paper, we improve the results of that algorithm adding a new constraint set based on the spatio-intensity neighborhood. We extend the algorithm to image sequences for multi-frame demosaicking and super resolution.