Edge-contrast-guided image interpolation using directional variation field diffusion

In this paper, a novel interpolation method is proposed, called the edge-contrast-guided interpolation (ECGI), which takes the edge contrast into consideration. Similar to the existing edge-guided methods, for those edge pixels under interpolation, the proposed ECGI will conduct the interpolation along their associated edge's direction. However, the novelty of our proposed ECGI lies in the treatment of non-edge pixels: We additionally view those non-edge pixels as `edge' pixels, if they are locating in the vicinity of true edges. In this case, each `edge' pixel is subject to conduct edge-guided interpolation along the same direction of the true edge that influences it. In our work, we use the edge contrast to determine how far a true edge could yield such influence on its nearby pixels; consequently, the stronger the contrast, the more pixels in its vicinity will be viewed as `edge' pixels. To implement this idea, we apply a variational approach over the directional variation (DV) fields to obtain new diffused DV fields for conducting edge-guided interpolation. Compared with the state-of-the-art interpolation methods, our method is able to deliver superior performance, yielding more clear edges without introducing ringing or other artifacts, while enjoying fairly low computational complexity.