A SURVEY ON IMAGE INTERPOLATION TECHNIQUES

To obtain more details in an image, image super-resolution (SR) technology is always desirable in visual information processing. It aims in reconstructing a high-resolution (HR) image from one or more low-resolution (LR) images. In recent times, methods of achieving image super-resolution have been the object of research. The efficient way is to explore the linear relationship among neighboring pixels to reconstruct a high-resolution image from a low-resolution input image. This paper uses low-rank matrix completion and recovery to determine the local order of the linear model implicitly. According to this theory, a method for performing single-image superresolution is proposed by formulating the reconstruction as the recovery of a low-rank matrix, which can be solved by the augmented lagrange multiplier method. In addition, the proposed method can be used to handle noisy data and random perturbations robustly.