Image demosaicking for Bayer-patterned CFA images using improved linear interpolation
Dongyan Wang, Gang Yu, Xiao Zhou, Chengyou Wang · 2017
With the development of digital imaging technique, the demosaicking algorithm becomes a hot spot in the field of image processing. An efficient interpolation method with good visual quality and less calculation amount is urgently needed. In this paper, an improved linear interpolation for demosaicking of Bayer-patterned color filter array (CFA) images is proposed. Compared with bilinear interpolation, the proposed scheme gives full consideration to brightness information and edge information of the image. Since different color components need to be interpolated, different size linear filters with different gain parameters are designed. We correct the bilinear interpolation by a correction value to estimate the unknown color. Experimental results show that the improved scheme achieves better performance than other two methods both in subjective assessment and objective assessment. It incurs much fewer false colors in high-frequency regions. The zipper effect is also weakened to some extent. Besides, the improved scheme reduces the computational complexity. Due to good real-time adaptability, it is easily implemented in hardware.