Image Zooming Based on Residuals

To improve the spatial resolution of image, many methods such as example-based and interpolation are presented nowadays. However, the smoothness of edges and the preserving of texture details in the zoomed image are still need to be improved to obtain better performance. Based on the residual correction and compensation idea, we propose a novel algorithm on image zooming, which refines the reconstructed residual using the Steer Kernel Regression and nonlocal filtering. Experimental results show that the proposed algorithm can not only well preserve the texture details but also enables the reconstructed image to achieve better visual effect and resolution.