A Super-Resolution Image Reconstruction using Triangulation Interpolation in Feature Extraction for automatic sign language recognition

Abstract

This paper presents the performance of the super-resolution visualization by Triangular Interpolation algorithm to extract characteristic in sign language recognition. By compares performance from sign language image files in the experiment, the results showed that the generation of the super-resolution image by improved Triangulation Interpolation technique can provide the best results when evaluating image performance using PSNR which has a similarity value between the original image and the high-resolution image. These use the PSNR method for measuring image quality. The PSNR value of sign language image is 40.6081 or has more efficiency at 13.15 percent when compared with the SRCNN techniques which are closest to the original image. For measuring performance by SSIM, which is a structured similarity measurement techniques, Triangulation Interpolation method can get the results of generating the super-resolution image next below the SRCNN technique. But in case of the real-time process, Triangulation Interpolation methods can process faster.