Shackleton, AM and Altahhan, AM orcid.org/0000-0003-1133-7744 (2019) A comparison study of deep learning techniques to increase the spatial resolution of photo-realistic images. In: ICONIP 2019: International Conference on Neural Information Processing. International Conference on Neural Information Processing, 12-15 Dec 2019, Sydney, NSW, Australia. Springer , pp. 341-348. ISBN 978-3-030-36807-4
Abstract
In this paper we present a perceptual and error-based comparison study of the efficacy of four different deep-learned super-resolution architectures, ESPCN, SRResNet, ProGanSR and LapSRN, all performed on photo-realistic images by a factor of 4x; adapting some of the current state-of-the-art architectures using Convolutional Neural Networks (CNNs). The resultant application and the implemented CNNs are tested with objective (Peak-Signal-to-Noise ratio and Structural Similarity Index) and perceptual metrics (Mean Opinion Score testing), to judge their relative quality and implementation within the program. The results of these tests demonstrate the effectiveness of super-resolution, showing that most network implementations give an average gain of +1 to +2 dB (in PSNR), and an average gain of +0.05 to +0.1 (in SSIM) over traditional Bicubic scaling. The results of the perception test also show that participants almost always prefer the images scaled using each CNN model compared to traditional Bicubic scaling. These findings also present a look into new diverging paths in super-resolution research; where the focus is now shifting from solely error-reduction, objective-based models to perceptually focused models that satisfy human perception of a high-resolution image.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2019. This is an author produced version of a conference paper published in ICONIP 2019: Neural Information Processing. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Nov 2020 12:11 |
Last Modified: | 06 Dec 2021 01:38 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-030-36808-1_37 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168212 |