Single image super-resolution via multiple linear mapping anchored neighborhood regression

The goal of learning-based image super-resolution (SR) is to generate a plausible and visually high-resolution (HR) image from a single low-resolution (LR) input image. The SR is an important branch of image reconstruction that concentrates on the improvement of image resolution and the problem is severely underconstrained. It’s to rely on examples or some strong image priors to reconstruct the missing HR image details. The paper addresses the problem of learning the mapping functions is, projection matrix between the LR and HR images based on a dictionary of LR and HR exemplars. In this paper, we present a self-learning single image SR method, which restores an HR image from self-examples extracted from the LR input image itself without relying on additional external training images. This paper proposes a novel computationally to further improve the SR performance of single image SR method that learns multiple linear mappings (MLM) based on SR method functions anchored neighborhood regression (ANR) to directly transform LR feature subspaces into HR subspaces. Moreover, we utilize segmentation flipped and rotated with a matrix-vector of the self-examples to expand the internal patch space. Furthermore, we set up MLM from the input LR features to the desire HR outputs to obtain stable. In particular, we improved SR efficiency using optimization principal component analysis (OPCA) based on dimensionality reduction to generate from low-dimensional into high-dimensional with matrix-vector patch space. For our proposed model, there are three regularization parameters analysis that require to optimize the training and refine the outcome. Experimental result indicates that our approach comparison on the standard benchmark with state-of-the-art method validation the effectiveness of our proposed.