• Dr. Shaik. Mahaboob Basha
• G.Roja
• D.Yamini
• J.Swetha
• T.Divya Bharathi

The general Super-Resolution based algorithms normally consider the distance related similarity parameter metrics. Inspite of the best optimum value selection of closest neighbors is of clearl significance for such strategies, the effect of appropriate measurements for Super-Resolution (SR) has been relinquished in existing techniques which we have gathered for evaluation of our proposed method. In this paper, we display a quick SR algorithm which fabricates builds neighborhood selection strategy and sub-linear structures. We play out an investigation of the idea of the parameters normally utilized for SR, observing that those parameters and features typically lie in the unitary hypersphere. In this way, we proposed a method which joins antipodal nature. The execution and performance of our method modified KPCA improves the quality in performance parameters namely Peak Signal to Noise Ratio (PSNR) and Mean Square Error ( MSE).

Super Resolution, Antipodal, Peak to Signal Noise Ratio, Mean Square Error

"MODIFIED KPCA TECHNIQUE FOR THE ESTIMATION OF METRICS FOR SUPER RESOLUTION BASED APPLICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 6, page no.549-553, June-2018, Available :http://www.jetir.org/papers/JETIR1806676.pdf

"MODIFIED KPCA TECHNIQUE FOR THE ESTIMATION OF METRICS FOR SUPER RESOLUTION BASED APPLICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 6, page no. pp549-553, June-2018, Available at : http://www.jetir.org/papers/JETIR1806676.pdf