一种降低复杂度的CCD彩色图像重构算法

A Low Computational Complexity Demosaicing Method for Bayer Pattern CCD

为了提高视频展台的图像质量和帧率,提出一种基于边缘检测的低复杂度插值方法。首先重构绿色分量图像,然后按照实验确定的阈值T划分图像平滑区域和边缘位置,分别对平滑部分、边缘位置进行双线性插值和Laplacian二阶校正项插值,来重构红、蓝色分量图像,最后对红蓝分量图像的平滑区域进行中值滤波以抑制伪彩失真。该算法与传统的两阶段图像重构算法相比,计算量显著降低,提高了图像重构实时性,并保持满意的图像质量,PSNR值达到33 dB以上。

In this paper, we propose an edge-detection based algorithm with lower computational complexity to improve image quality and increase rate of real video. With this algorithm, all the missing green values can be reconstructed in Bayer pattern image by using edge-detected interpolation method. Reconstructed images composed of green pixels are classified according to the high frequency components in image, and the threshold T needed for all kinds of green images in the edge detection is determined through experiments. The simple bilinear interpolation is used for the smooth area while the Laplacian interpolation with the second-order correction term is adopted to reconstruct the other red/blue values on the edge. This algorithm resolves effectively conflicts between reconstructing high quality color image and reducing computational complexity, and thus largely enhances processing speed for reconstructing satisfied color image whose PSNR value is up to 33dB.