Computer Science ›› 2015, Vol. 42 ›› Issue (11): 104-107.doi: 10.11896/j.issn.1002-137X.2015.11.022

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Single-frame Image Super-resolution Reconstruction Algorithm Based on Nonnegative Neighbor Embedding and Non-local Means Regularization

PENG Yang-ping, NING Bei-jia and GAO Xin-bo   

  • Online:2018-11-14 Published:2018-11-14

Abstract: 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 2 times 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.

Key words: Super-resolution reconstruction,Non-local means,Neighbor embedding,Regularization

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