Pixelwise dictionary learning based localityconstrained representation for noise robust face hallucination Abstract   Recently, the sparsity and locality constrained linear coding (SLLC) attracted much attention in image superresolution (SR) applications. However, the current SLLC based SR methods are too weak to handle the impulse noise problem. Therefore, this work presents a dictionary learningbased localityconstrained representation (DLcR) for robust face hallucination. It simultaneously hallucinates the face images and suppresses the noise (outliers) using the noisy pixel information in the observed faces. It first identifies the correct position of the noisy pixels in the input face image and then learns the positionwise lowresolution (LR) dictionary images with the detected noise. This learning makes the similar noisy structure of LR dictionary images as the input LR face, which minimizes the error in optimal weight reconstruction. In addition, the performance of DLcR is improved further by imposing the appropriate thresholds on the LR dictionary, named TDLcR. The thresholding of the LR dictionary in TDLcR leads to represent the input LR patch through its nearest LR dictionary patches with the precise reconstruction weights. The comparison results of TDLcR with several positionpatch based face SR methods and recent deep learningbased SR method show its superiority on standard as well as realworld face datasets.

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.