LAUSR

In recent years, many deep-network-based super-resolution techniques have been proposed and have achieved impressive results for 2${\times }$ and higher magnification factors. However, lower magnification factors encountered in some industrial applications have not received special attention, such as 720P-to-1080P (1.5${\times }$ magnification). Compared to traditional 2${\times }$ or higher magnification factors, these lower magnifications are much simpler, but reconstructions of high-definition images are time-consuming and computationally complex. Hence, in this paper, a fast image upsampling method is designed specifically for industrial applications at low magnification. In the proposed method, edge and nonedge areas are first distinguished and then reconstructed via different fast approaches. For the edge area, a local edge pattern encoding-based method is presented to recover sharp edges. For the nonedge area, a global iterative reconstruction with texture constraint is utilized. Moreover, some acceleration strategies are also presented to further reduce the complexity. The experimental results demonstrate that the proposed method can obtain performance comparable to that of some state-of-the-art methods for 720P-to-1080P magnification, but the computational cost is much lower.