An Improved Super-resolution Reconstruction Algorithm with Locally Linear Embedding
Xiuchang Zhu · Journal of Nanjing University of Posts and Telecommunications · 2013
An improved super resolution reconstruction algorithm is proposed with locally linear embedding.The improvement includes three aspects.Firstly,the DCT coefficients of low resolution image patches are taken as the feature representative instead of the first order and second order gradients,which will reduce the effect of noise.Secondly,the number of adjacent blocks is chosen adaptively according to the relationship between the input low resolution image patch and its neighbors,which will avoid the possibility of choosing a distant patch as neighbor.Thirdly,the training sample of the high resolution image is taken as the residual image resulting from the difference between the high resolution image and the corresponding low resolution one.This can not only avoid the disturbance of low frequency components,but also reduce the number of smoothness computation.The experimental results have shown that the improved algorithm can achieve a better reconstruction effect with improved PSNR of 4.07 dB and improved SSIM of 0.0654 compared to the existing LLE algorithm,and improved PSNR of 0.62 dB and improved SSIM of 0.0066 compared to the sparse representation algorithm.In addition,using DCT coefficients as the feature representative reduces the computational complexity in that the number of the extracted features needed is only a quarter of that using the first order and second order gradients.