Image Super-Resolution Improved by Edge Information

As well as in other knowledge domains, deep learning techniques have revolutionized the development of image super-resolution approaches. State-of-the-art algorithms for this problem have employed convolutional neural networks in residual architectures with a number of layers and generic loss functions, such as L1 and Peak Signal-to-Noise Ratio (PSNR). These frameworks (architectures + loss functions) are generic and do not address the main characteristics of an image for human visual perception (luminance, contrast, and structure) resulting in better images, however, with noise mainly at the edges. In this work, we present an edge enhanced super-resolution (EESR) method using a novel residual neural network with focus on image edges and a mix of loss functions that use PSNR, L1, Multiple-Scale Structural Similarity (MS-SSIM), and a new loss function based on the pencil sketch technique. As main contribution, the proposed framework aims to leverage the limits of image super-resolution and presents an improvement of the results in terms of the SSIM metric and achieving competitive results for the PSNR metric.