Image Magnification with Multi-Level Contour Constraints

Effective edge sharpening in image enlargement is always a difficult problem in image interpolation, and in order to solve this problem, an image enlargement algorithm with multi-level contour constraints is proposed. The detection operator is used to preprocess the image, and the image is divided into edge region and flat region. Adaptive gradient diffusion is applied to the edge region of the image to obtain an appropriate edge contour layer as an image magnification constraint. Finally, the contour layer is re-sampled directly through curve interpolation without adding additional edge layers to ensure that the enlarged image has clear visual edges. For the flat area of the non-contour layer, the bi-cubic Coons interpolation surface is constructed and resampled to maintain the smoothness of the flat region. The test images are natural images and medical images. The source of natural images is set5 and set14 test sets. The experimental comparison is mainly made from three aspects: objective effect, visual effect and time complexity. The experimental results show that the enlarged image obtained by the new algorithm can not only keep the contour clear, but also the PSNR and SSIM indexes exceed most classical interpolation algorithms and the popular machine learning-based algorithms.