Super Resolution for Noisy Images Using Convolutional Neural Networks

Zaid Bin Mushtaq, Shoaib Mohd Nasti, Chaman Verma, Maria Simona Raboaca, Neerendra Kumar and Samiah Jan Nasti
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Zaid Bin Mushtaq: Department of Information Technology, Central University of Kashmir, Ganderbal 191201, India
Shoaib Mohd Nasti: Department of Information Technology, Central University of Kashmir, Ganderbal 191201, India
Chaman Verma: Department of Media and Educational Informatics, Faculty of Informatics, Eötvös Loránd University, 1053 Budapest, Hungary
Maria Simona Raboaca: ICSI Energy, National Research and Development Institute for Cryogenic and Isotopic Technologies, 240050 Ramnicu Valcea, Romania
Neerendra Kumar: Department of Computer Science and Information Technology, Central University of Jammu, Jammu 181143, India
Samiah Jan Nasti: Department of Computer Sciences, Baba Ghulam Shah Badshah University, Rajouri 185234, India

Mathematics, 2022, vol. 10, issue 5, 1-18

Abstract: The images in high resolution contain more useful information than the images in low resolution. Thus, high-resolution digital images are preferred over low-resolution images. Image super-resolution is one of the principal techniques for generating high-resolution images. The major advantages of super-resolution methods are that they are economical, independent of the image capture devices, and can be statically used. In this paper, a single-image super-resolution network model based on convolutional neural networks is proposed by combining conventional autoencoder and residual neural network approaches. A convolutional neural network-based dictionary method is used to train low-resolution input images for high-resolution images. In addition, a linear refined unit thresholds the convolutional neural network output to provide a better low-resolution image dictionary. Autoencoders aid in the removal of noise from images and the enhancement of their quality. Secondly, the residual neural network model processes it further to create a high-resolution image. The experimental results demonstrate the outstanding performance of our proposed method compared to other traditional methods. The proposed method produces clearer and more detailed high-resolution images, as they are important in real-life applications. Moreover, it has the advantage of combining convolutional neural network-based dictionary learning, autoencoder image enhancement, and noise removal. Furthermore, residual neural network training with improved preprocessing creates an efficient and versatile single-image super-resolution network.

Keywords: convolution neural network; image enhancement; noisy image; super resolution (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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