Projection onto the convex sets model based on non-downsampling contourlet transform and high-frequency iteration

Obtaining high-resolution images is one of the critical issues in many areas. The super-resolution (SR) algorithm is a method of obtaining a high-resolution image from low-resolution images. The projection onto the convex sets (POCS) iteration method is an essential spatial multi-image SR algorithm. In addition to generating Gibbs artefacts, the traditional POCS algorithm has some restrictions on the image source, such as the signal-to-noise ratio and the resolution of the reference frame. In order to remove the limitation of image source, eliminate artefact, and get more edge information from iteration. This Letter proposes a new POCS iterative model. This Letter uses the high-frequency (HF) image obtained after a non-downsampling contourlet transform as the iteration object. It uses the unique attributes of the HF image to establish a new constrained convex set to construct the iterative formula. In order to make the iteration effect more apparent, this Letter has Laplace enhancement to the reference frame selected by principal component analysis. The experimental results show that the results obtained by the proposed algorithm are satisfactory in both subjective and objective evaluations.