Image interpolation via collaging its non-local patches

Interpolation is an important problem in image processing. The main issue on this application is to recover high frequency components lost by aliasing. In this paper, a novel spatial interpolation method exploiting the non-local redundancy of image is proposed, where the high-resolution (HR) image can be reconstructed by collaging the patches of its low-resolution (LR) observation. The appeal in this work is its simplicity, with no requirement of solving complex optimization equations. Simulation results suggest that the proposed method achieves a very competitive performance in both subjective visual quality and objective image quality (in terms of PSNR and structural similarity index measurement (SSIM)), compared to some recently proposed structured sparse representation-based methods.