Single-frame Image Super-resolution Reconstruction Algorithm Based on Nonnegative Neighbor Embedding and Non-local Means Regularization
Peng Yang-pin · 2015
Single-frame image super-resolution(SR)reconstruction aims to obtain a high-resolution(HR)image from a low-resolution(LR)input image.To overcome the limitations of traditional neighbor-embedding-based algorithm,we proposed a single-frame image super-resolution reconstruction algorithm based on nonnegative neighbor embedding and non-local means regularization.In the training phase,the LR images are magnified 2times at first,leading to better preservation of neighborhood between LR and HR images in case of high magnification factor.In the reconstruction phase,non-negative neighbor embedding is employed to select neighborhood number effectively.Finally,a non-local means regularization term is introduced into the final reconstruction process by taking advantage of the non-local similarity between natural image patches.Experimental results demonstrate that the proposed method can achieve results with richer textures and sharper edges compared with those from traditional methods.