Interpolation Based Image Super Resolution by Support-Vector-Regression

Abstract — The higher resolution image can be reconstructed from lower resolution images using Super-Resolution (SR) algorithm based on Support Vector Regression (SVR) by combining the pixel intensity values with local gradient information. Support Vector Machine (SVM) can construct a hyperplane in a high or infinite dimensional space which can be used for classification. Its regression version, Support Vector Regression (SVR) has been used in various image processing tasks. In this paper, we present the SR algorithm in MATLAB and Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) is measured and compared