Iterated Back Projection Based Super-Resolution for Iris Feature Extraction

In this paper, Iterated Back Projection and median estimators super-resolution algorithms, are implemented to increase the resolution of low resolution (LR) iris images. These two algorithms are analysed by extracting the Gray Level Co-occurrence Matrix (GLCM) features of super resolute iris images. The GLCM features of super-resolute iris images are compared with GLCM features of original iris images. The quality of resolution of enhanced image is measured using different image quality measures. It has been seen that the GLCM features of reconstructed images using above algorithms matches with that of original iris image and also the quality of enhanced image remains same.