Nonlocal low-rank matrix completion for image interpolation using edge detection and neural network

In this paper, we propose a nonlocal low-rank matrix completion method using edge detection and neural network to effectively exploit the nonlocal inter-pixel correlation for image interpolation and other possible applications. We first interpolate the images using some basic techniques, such as bilinear and edge-directed methods. Then, each image patch is categorized as smooth regions, edge regions, or texture regions and adaptive interpolating mechanisms are applied to each specific type of regions. Finally, for each specific type of regions, neural networks and low-rank matrix completion are employed to accurately update the results. An iteratively re-weighted minimization algorithm is used to solve the low-rank energy minimization function. Our experiments on benchmark images clearly indicate that the proposed method produces much better results than some existing algorithms using a variety of image quality metric in terms of both objective image quality assessment and subjective quality assessment.