WITHDRAWN: Residual Dense Convolutional Neural Network for Image Super-Resolution

Abstract

Abstract Image super-resolution (SR) is the resource-intensive process of scaling low-resolution (LR) images to high resolution (HR). There are a number of difficulties with SR and related areas of computer vision, so it is crucial that we develop effective SR techniques. The evolution of deep learning technology, especially convolutional neural networks (CNNs), has led to a number of approaches to image improvement, including image SR. The aim of this paper is to develop a fast image SR technique based on a CNN. We introduce a tripartite CNN for multi-color SR enhancement. First, we develop a deconvolution step at the end of the network structure, and mapping is then applied to the input LR luminance, which is used as a reference for the HR image. Second, a novel residual dense network is developed to exploit the hierarchical features of the convolutional layers. This step merges different local features using densely connected convolutional layers. Third, we apply guided filters to improve the chromatic interpolation. Guided filters identify sharp edges and fine details in the reference image and reproduce them in the output images. Finally, we extract further mapping layers using smaller filters. Our experimental results confirm that this algorithm achieves visual and quantitative results superior to other state-of-the-art methods while maintaining computational efficiency.