Super-resolution using lightweight detailnet network

Abstract

Image Super-Resolution is a complex method capable of converting a low-resolution image to a high-resolution image. Regarding many challenges of the Super-Resolution problem and a wide variety of its applications in image processing and man’s interpretations, it is very crucial to find an operational method. Development of deep learning methods, especially Convolutional Neural Networks has increased the power of image enhancement methods including image Super-Resolution. The goal of this research is to propose a fast speed image Super-Resolution method using Convolutional Neural Networks. The proposed DetailNet network has a small structure to prevent the problems of training very deep networks. Super-Resolution is fast by this method due to its small simple network structure. The DetailNet network is designed to add details to an input image. According to the results, DetailNet has a significant ability to produce image details. This network has a general function. Therefore, the low-resolution image size can be increased first using any method, then DetailNet can enhance the image quality and add more details to the input image. The Proposed method is applied to natural color images which achieved acceptable results on benchmark datasets.