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.