Image Super-Resolution Reconstruction via L1/2 and S1/2 Regularizations - Liang Yong

Compressed sensing (CS) theory has attracted much attention in the field of signal and image processing. In this paper, the ideas and methods of the image super-resolution (SR) reconstruction combined with CS is studied. The challenge is how to reconstruct a SR image when only one low- resolution (LR) image is available. Regularization methods are the important image SR techniques. Recently, the transform-invariant directional total variation approach with transform-invariant low-rank textures based on Schattenp=1 and L1-norm penalties (TI-DTV+TILT1) has capability of achieving high-quality SR at up-sampling factors. In this paper, we investigate a novel TI-DTV model with TILT based on Schattenp=1/2 (S1/2 -norm) and L1/2-norm penalties (TILT1/2). Moreover, inspired by the alternating direction method of multipliers (ADMM), we propose the alternating threshold iterative algorithm for the new model …