Sparsity-based defect pixel compensation for arbitrary camera raw images
Sparsity-based defect pixel compensation for arbitrary camera raw images
复制标题
针对任意相机原始图像的基于稀疏性的缺陷像素补偿
DOI：
10.1109/icassp.2011.5946639
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
André Kaup
中科院分区：
文献类型：
--
作者：
M. Schöberl;Jürgen Seiler;Bernhard Kasper;S. Fößel;André Kaup
在高质量成像中，即使是像单个像素一样小的微小失真也是可见的，并且不能被接受。虽然CMOS图像传感器的生产质量非常高，但为了合理的产量，我们仍然需要接受大型图像传感器中的一些缺陷像素和缺陷簇。在本文中，我们将比较补偿算法的原始图像传感器数据。我们提出了一种新的方法，稀疏性假设的基础上，优于现有的缺陷补偿算法。此外，我们提出的插值算法是通用的，并不适用于拜耳模式的图像。它可以直接应用于任何规则的彩色滤光片图案或灰度图像。我们的例子表明，图像传感器与大集群的缺陷仍然可以用于生成高质量的图像。
In high quality imaging even tiny distortions as small as a single pixel are visible and can not be accepted. Although the production quality of CMOS image sensors is very high, for reasonable yields we still need to accept some defect pixels and clusters of defects in large image sensors. In this paper we will compare compensation algorithms for raw image sensor data. We propose a new approach based on the sparsity assumption that outperforms existing defect compensation algorithms. Furthermore, our proposed interpolation algorithm is universal and not at all adapted to Bayer pattern images. It can directly be applied to any regular color filter pattern or gray scale image. Our examples show, that image sensors with large clusters of defects can still be used for the generation of high quality images.