Nonlocal similarity edgeguided based semicoupled dictionary learning super resolution In this paper, we propose a novel edge preserving and noise adaption for retina image superresolution (SR) reconstruction. The proposed method incorporates nonlocal similarity and edge difference into semicoupled dictionary learning (NSEDSCDL). Firstly, in order to suppress speckle noise during reconstructing, nonlocal similarity is utilized to construct the denoising constraint. Secondly, for preserving the edge information of the reconstructed image, the edge difference between the observed lowresolution (LR) image and degraded version of the reconstructed image is employed to construct regularization term. Thirdly, we explore the adaptive coefficients of edge constraint to find the optimal edge information during optimizing the objective function. Experiments on retina images demonstrate that the proposed algorithm outperforms other stateoftheart methods, especially for the noise retina images with weak edges.

Superresolution with Nonlocal Regularized Sparse Representation The reconstruction of a high resolution (HR) image from its low resolution (LR) counterpart is a challenging problem. The recently developed sparse representation (SR) techniques provide new solutions to this inverse problem by introducing the l 1 norm sparsity prior into the superresolution reconstruction process. In this paper, we present a new SR based image superresolution by optimizing the objective function under an adaptive sparse domain and with the nonlocal regularization of the HR images. The adaptive sparse domain is estimated by applying principal component analysis to the grouped nonlocal similar image patches. The proposed objective function with nonlocal regularization can be efficiently solved by an iterative shrinkage algorithm. The experiments on natural images show that the proposed method can reconstruct HR images with sharp edges from degraded LR images.