Low Cost Edge-Based Image Interpolation Method Using First- and Second-Order Edge Detector Information

Image interpolation plays an important role in many computer vision and image processing tasks, such as image resizing, denoising, and restoration. Most traditional interpolation algorithms work on the time domain and deal with all image regions in a similar manner and do not differentiate between edges and smooth areas, resulting in blurring effects. To achieve high efficiency, all these methods are complex and time-consuming. To tackle these challenges, this work offers a low-cost image interpolation algorithm and a high-quality image-scaling method. The algorithm starts by applying edge detection operators to estimate detailed sub-bands that are required by the inverse WT to construct high-quality scaled images with low-cost calculations. The algorithm is evaluated on twenty different datasets including 5500 images overall. The results indicate the high restoration quality of the proposed algorithm compared to state-of-the-art techniques. The proposed algorithm achieved the highest average SSIM and PSNR values of 0.998 and 50 dB, respectively. Moreover, the rational cost of the proposed work was reduced to 1.75 compared to the highest existing method with a rational cost of 8437.