Paper Title
Application of Kernel Regression in Single Image Super Resolution

Super resolution is an enhancement technique used for converting one or more low quality image in high quality image. Single image is effectively resolved with two methods interpolation based and learning based method. Traditional interpolation methods are unable to produce sharp edges and clear details. So learning based method is applied in this paper. Second order kernel regression is used at the output of testing and training phase to reduce the mapping error. Training features are extracted using K-singular value decomposition dictionary. Kernel allow data to map into high dimensional for increasing computational efficiency. This method is capable to reduce use of dictionary. Comparison between old method and presented method were done based on peak signal to noise ratio and mean square error values. Result of experimentation shows that implemented SR method is more efficient and robust to different data types. Result shows that implemented SR method has much future scope with high resolution factor. Keywords - Kernel regression, K-singular value decomposition, steering matrix