Department of Embedded Systems Engineering, University of Incheon, Incheon 406-772, Korea.
IEEE Trans Image Process. 2013 Jan;22(1):146-56. doi: 10.1109/TIP.2012.2214041. Epub 2012 Aug 17.
This paper adapts the least-squares luma-chroma demultiplexing (LSLCD) demosaicking method to noisy Bayer color filter array (CFA) images. A model is presented for the noise in white-balanced gamma-corrected CFA images. A method to estimate the noise level in each of the red, green, and blue color channels is then developed. Based on the estimated noise parameters, one of a finite set of configurations adapted to a particular level of noise is selected to demosaic the noisy data. The noise-adaptive demosaicking scheme is called LSLCD with noise estimation (LSLCD-NE). Experimental results demonstrate state-of-the-art performance over a wide range of noise levels, with low computational complexity. Many results with several algorithms, noise levels, and images are presented on our companion web site along with software to allow reproduction of our results.
本文将最小二乘亮度-色度解复用（LSLCD）去马赛克方法应用于噪声的拜耳彩色滤光片阵列（CFA）图像。为白平衡伽马校正 CFA 图像中的噪声建立了一个模型。然后开发了一种方法来估计每个红色、绿色和蓝色颜色通道中的噪声水平。基于估计的噪声参数，从一组有限的配置中选择一个适应特定噪声水平的配置来对噪声数据进行去马赛克。这种噪声自适应去马赛克方案称为具有噪声估计的最小二乘亮度-色度解复用（LSLCD-NE）。实验结果表明，在很宽的噪声水平范围内，该方案具有较高的性能，且计算复杂度较低。在我们的配套网站上，提供了许多具有多种算法、噪声水平和图像的结果，并提供了允许重现我们结果的软件。
