Color demosaicking is critical to the image quality of single-sensor-based imaging devices. Caused by the sampling pattern of color filter array (CFA), the demosaicked images typically suffer from visual color artifacts in regions of high frequency and sharp edge structures, degrading the quality of camera output. We present a new high-quality demosaicking algorithm by taking advantage of deinterlacing and median-based filtering techniques. We treat the sampled green data of Bayer CFA as a form of diagonal interlaced green planes and make use of some key concepts about spatial deinterlacing to help the edge estimation in terms of both various directions and accuracy. In addition, a specific edge feature, sharp line edge of width 1 pixel, can also be handed well by the proposed method. The median-based filtering techniques are developed for suppressing most visual demosaicking artifacts, such as zipper effect, false color artifact, and interpolation artifact. Experimental results show that our algorithm is effective in suppressing visual artifacts, preserving the edges of image with sharpness and satisfying visual inspection, while keeping computational efficiency.
Demosaicking is an interpolation process that transforms a color filter array (CFA) image into a full-color image in a single-sensor imaging pipeline. In all demosaicking techniques, the interpolation of the green components plays a central role in dictating the visual quality of reconstructed images because green light is of maximum sensitivity in the human visual system. Guided by this point, we propose a new soft-decision demosaicking algorithm using directional filtering and embedded artifact refinement. The novelty of this approach is twofold. First, we lift the constraint of the Bayer CFA that results in the absence of diagonal neighboring green color values for directionally recovering diagonal edges. The developed directional interpolation method is fairly robust in dealing with the four edge features, namely, vertical, horizontal, 45-deg diagonal, and 135-deg diagonal. In addition, the proposed embedded refinement scheme provides an efficient way for soft-decision-based algorithms to achieve improved results with fewer computations. We have compared this new approach to six state-of-the-art methods, and it can outstandingly preserve more edge details and handle fine textures well, without requiring a high computational cost.