Fast Single Image Super Resolution Reconstruction via Image Separation

Abstract

In this work, a fast single image super resolution reconstruction (SRR) approach via image separation has been proposed. Based on the assumption that the edges, corners, and textures in the image have different mathematical models, we apply different image SRR algorithms to process them individually. Thus, our approach is divided into three steps: 1) separating the given image into cartoon and texture subcomponent by nonlinear filter based image decomposition technique; 2) using improved local-self similarity model based algorithm to interpolate the cartoon subcomponent and the wavelet domain Hidden Markovian Tree (HMT) model based algorithm to zoom the texture subcomponent; and 3) fusing the interpolated cartoon and texture subcomponents together to derive the recovered high-resolution images. Since the decomposition and super resolution algorithms in the proposed approach are mainly based on simple convolution and linear algebra computations, its efficiency can be guaranteed. Also, the simulated and real-life images experiments can validate the performance of our proposed algorithm when compared with some other state-of-the-art super-resolution approaches.Index Terms-single image super-resolution, image separation, local-self similarity model, wavelet domain HMT model

Effective Mean Square Differences: A Matching Algorithm for Highly Similar Sheet Metal Parts

“…When downsampling the contour edge of the target, this method can make the Both image downsampling and rotation create jagged edges in the image contour, as shown in Figure 4. Therefore, in the process of image downsampling, retaining the image details as much as possible while reducing the image resolution is challenging [31]. To solve this problem, an interpolation method is required to fill the pixel gap and reduce its impact on the image edge information.…”