Open AccessLearning Based Resolution Enhancement of Digital Images
Author(s) -
Jebaveerasingh Jebadurai,
Immanuel Johnraja Jebadurai,
Getzi Jeba Leelipushpam Paulraj,
Nancy Emymal Samuel
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f9025.088619
Subject(s) - artificial intelligence , computer science , convolutional neural network , preprocessor , metric (unit) , resolution (logic) , image (mathematics) , generalization , computer vision , process (computing) , pattern recognition (psychology) , noise (video) , digital image , image quality , image processing , mathematics , engineering , mathematical analysis , operations management , operating system
Image super-resolution (SR), the process that improves the resolution, has been used in many real world applications. SR is the preprocessing phase of majority of these applications. The improvement in image resolution improves the performance of image analysis process. The SR of digital images take the low resolution images as inputs. In this article, a learning based digital image SR approach is proposed. The proposed approach uses Convolutional Neural Network (CNN) with leaky rectified linear unit (ReLU) for learning and generalization. The experiments with the test dataset from USC-SIPI indicate that the proposed approach increases the quality of the images in terms of the quantitative metric peak signal to noise ratio. Further, it avoided the problem of dying ReLU.