A HVS-guided approach for real-time image interpolation

In this paper, we propose a novel interpolation algorithm for adapting the human visual system (HVS) and applying in real-time image upscaling. The defined statistical features are first computed in a local window of the low-resolution (LR) counterpart. Then the most correlated neighbours of a missing pixel in high-resolution (HR) image are adaptively selected based on local structural analysis for the prominent edges and fine textures. Finally, the unknown pixel values are estimated through a designed directional clustering model (DCM) which incorporates HVS information into the weighted coefficients. The extensive experimental results show that the proposed image interpolation method can accurately reconstruct the structures of HR image in term of arbitrary magnification factors and effectively suppress the jaggy/ringing artifacts with low computation complexity.