Efficient edge-oriented based image interpolation algorithm for non-integer scaling factor
Though image interpolation has been developed for many years, most of state-of-the-art methods, including machine learning based methods, can only zoom the image with the scaling factor of 2, 3, 2k, or other integer values. Hence, the bicubic interpolation method is still a popular method for the non-integer scaling problem. In this paper, we propose a novel interpolation algorithm for image zooming with non-integer scaling factors based on the gradient direction. The proposed method first estimates the gradient direction for each pixel in the low resolution image. Then, we construct the gradient map for the high resolution image by the spline interpolation method. Finally, the intensity of missing pixels can be computed by the weighted sum of the pixels in the pre-defined window. To preserve the edge information during the interpolation process, the weight is determined by the inner product of the estimated gradient vector and the vector from the missing pixel to the known data point. Simulations show that the proposed method has higher performance than other non-integer time scaling methods and is helpful for superresolution.