Neighbor Embedding based Super-Resolution using Residual Luminance

Resolution plays a crucial role for study of information in an image. Therefore to enhance the resolution of an image, there are so many techniques have been proposed with respect to the reference images. In this paper, we proposed a new scheme for single image super-resolution based on the neighbor embedding method. Many feature selection methods have been proposed for the learning based super-resolution using manifold learning. Here a new feature selection has been proposed by combining first-order gradient and residual of the luminance component, inspired by Gaussian pyramid. In this Neighbor Embedding based Super-Resolution using the Residual Luminance (NESRRL) method the high resolution targeted image is estimated by the training image pairs. This approach imposes the local compatibility and smoothness constraints between patches in the estimated high resolution image. The experimental results show the comparisons of qualitative performance of proposed method with different existing methods using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM).