An Improved Edge-directed Image Interpolation Algorithm
一种改进的边缘方向插值算法

Regarding the inherent drawbacks of linear interpolation (edge blurring or artifact), nonlinear interpolation technique is studied in this paper. Existing new edge-directed interpolation algorithm utilizes the duality between high-resolution covariance and its low-resolution counterpart to realize adaptive nonlinear interpolation and obtains a much better visual quality of interpolated images than traditional linear interpolations. But interpolated images may still suffer from blurring edges or introducing artifacts around edge area. To achieve a better subjective quality, an improved algorithm is proposed in this paper. It makes full use of local relative information in the low-resolution image. Six downsampled neighbors are involved to estimate local covariance characteristics at high resolution. Two important applications of the algorithm are investigated: resolution enhancement of grayscale images and reconstruction of color images. Experimental results demonstrate that the improved algorithm not only decreases the computational complexity but also succeeds in reducing artifacts on the edges and substantially improves the visual quality of the interpolated images over conventional linear interpolation.