Abstract
Super-resolution has been a popular research topic in the image processing area. It is a process of getting a high-resolution image from one or multiple low-resolution images. We focus on the super resolution method that produces a high resolution image from a single low resolution image, which is method with self-similarity image pyramid. In the algorithm, we can find the similar patch in the image pyramid to patch up the correspond patch to obtain a high resolution image. However, the method could result in unrealistic shape and edge on the certain object in the reconstructed image due to the insufficient patch and the error propagation. To overcome the problem, we use the framework of self-similarity image pyramid and compared with weighted interpolation method in each layer, and improve upper layers’ accuracy by reducing lower layers’ error. Our experimental results show that proposed method could reach higher PSNR value than some existing methods and improve visual quality in the reconstructed image.