We present an improved directional weighted interpolation method for single-sensor camera imaging. By observing the fact that the conventional directional weighted interpolation methods are based on unreliable assumptions using spectral correlation, a contribution of this work is made using an antialiasing finite impulse response filter to improve the interpolation accuracy by exploiting robust spectral correlation. We also make a contribution toward refining the interpolation result by using the gradient inverse weighted filtering method. An experimental analysis of images revealed that our proposed algorithm provided superior performance in terms of both objective and subjective image quality compared to conventional directional weighted demosaicking algorithms. Our implementation has very low complexity and is, therefore, well suited for real-time applications.
We propose a local region statistics-based weighted interpolation filter for intrafield deinterlacing. The proposed algorithm consists of three steps: first, we preinterpolate the missing line with an efficient interpolation filter in the working window. Then, we calculate the coefficients of the center-weighted interpolation filter by exploiting local statistics, namely the center-weighted mean, the center-weighted variance, and the closeness of the neighboring pixels. In the last step, we interpolate the missing line using the proposed filter. Experimental results show that the proposed algorithm provides superior performances in terms of both objective and subjective image qualities than the existing algorithm.