• Mr. Joy Acharya
• Nirav J. Chauhan

Recently image super resolution (SR) has become an important research area to generate high resolution (HR) image from given low resolution (LR) image. The main aim of super resolution is to improve visual quality of available low resolution image. So the existing low resolution imaging systems can be used. The need for extracting high quality image arises in the fields of medical imaging, remote sensing, biometrics identification and pattern recognition. In this paper we have focused on single image super resolution where only one low resolution image of the scene is available.We have approached SR using learning based techniques. We present a novel self-learning approach with multiple kernel learning for adaptive kernel selection for SR. The Multiple Kernel Learning is theoretically and technically very attractive, because it learns the kernel weights and the classifier simultaneously based on the margin criterion. With theoretical supports of kernel matching search method and Optimization approach (Gradient) are proposed our SR framework learns and selects the optimal Kernel ridge regression model when producing an SR image, which results in the minimum SR reconstruction error. We evaluate our method on avariety of images, and obtain very promising SR results. In most cases, our method quantitatively and qualitatively outperforms bi-cubic interpolation and state-of-the-art learning based SR approaches.

image super resolution (SR) ,Multiple Kernel Learning

"Implementation and Optimization of Learning Approach for Super Resolution.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.2, Issue 5, page no.1411-1417, May-2015, Available :http://www.jetir.org/papers/JETIR1505015.pdf

"Implementation and Optimization of Learning Approach for Super Resolution.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.2, Issue 5, page no. pp1411-1417, May-2015, Available at : http://www.jetir.org/papers/JETIR1505015.pdf