Abstract
The introduction of neural networks and deep learning models in the field of image processing brought significant progress and achievements. These CNN networks are used in many ways in image processing for classification, segmentation and region of interest detection. They are mainly used in real-time applications like face detections, YOLO, resolution enhancement, etc. The major attention of this paper will be in image enhancement which has wide applications in fields like satellite images, surveillance, etc. The resolution enhancement has been solved in the past using arithmetic operations and has addressed the issue to some extent. The techniques like nearby neighbor and bicubic interpolation (Gao in, Opt. Express 19:26,161–26,173, 2011, [1]) were able to enhance images but were not much reliable in the real applications. With the introduction of GAN (Zhang et al., in [2]) into image processing, the problem of image resolution has been tackled to a certain level. The methods such as EDSR ( Lim et al., in [3]), SR-GAN (Ledig et al., in [4]) and VDSR (Kim et al., [5]) have introduced the CNN for addressing this issue. The SR-GAN model is currently the state of art in the field of image resolution enhancement and delivers a PSNR of 29.40 on Set5 dataset and 25.16 on BSD100 dataset. In this work, we are going to take the method of SR-GAN and address the problem that are left unsolved in the SR-GAN method and improves the PSNR and SSIM score with minor changes in the methodology.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
S. Gao, V. Gruev, Bilinear and bicubic interpolation methods for division of focal plane polarimeters. Opt. Express 19(27), 26161–26173 (2011)
H. Zhang et al., Self-attention generative adversarial networks, in International Conference on Machine Learning (2019), pp. 7354–7363
B. Lim et al., Enhanced deep residual networks for single image super-resolution, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2017), pp. 136–144
C. Ledig et al., Photo-realistic single image super-resolution using a generative adversarial network, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 4681–4690
J. Kim, J.K. Lee, K.M. Lee, Accurate image super-resolution using very deep convolutional networks, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 1646–1654
P. Gopika, et al., Single-layer convolution neural network for cardiac disease classification using electrocardiogram signals, in Deep Learning for Data Analytics (Academic Press, 2020), pp. 21–35
P. Gopika, et al., Transferable approach for cardiac disease classification using deep learning, in Deep Learning Techniques for Biomedical and Health Informatics (Academic Press, 2020), pp. 285–303
S.O. Patil, V.V. Sajith Variyar, K.P. Soman, Speed bump segmentation an application of conditional generative adversarial network for self-driving vehicles, in 2020 4th International Conference on Computing Methodologies and Communication (ICCMC) (IEEE, 2020)
T.K. Arun Kumar, et al., Convolutional neural networks for fingerprint liveness detection system, in 2019 International Conference on Intelligent Computing and Control Systems (ICCS) (IEEE, 2019)
I. Frosio, J. Kautz, Statistical nearest neighbors for image denoising. IEEE Trans. Image Process. 28(2), 723–738 (2019)
Z. Dengwen, An edge-directed bicubic interpolation algorithm, in 2010 3rd International Congress on Image and Signal Processing, vol. 3. (2010), pp. 1186–1189
S. Hitawala et al., Image super-resolution using VDSR-ResNeXt and SRCGAN. arXiv preprint arXiv:1810.05731 (2018)
M.S.M. Sajjadi, B. Scholkopf, M. Hirsch, EnhanceNet: single image super-resolution through automated texture synthesis, in 2017 IEEE International Conference on Computer Vision (ICCV) (2017), pp. 4501–4510
D. Martin et al., A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, in Proceedings 8th IEEE International Conference on Computer Vision, vol. 2. (2001), pp. 416–423
E. Agustsson, R. Timofte, NTIRE 2017 challenge on single image super-resolution: dataset and study, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2017)
C. Ye et al., Network deconvolution, in arXiv preprint arXiv:1905.11926 (2019)
Karen Simonyan and Andrew Zisserman. “Very deep convolutional networks for large-scale image recognition”. In: arXiv preprint arXiv:1409.1556 (2014).
E. Agustsson, R. Timofte, Ntire 2017 challenge on single image super-resolution: dataset and study, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 126–135
F.A. Fardo et al., A formal evaluation of PSNR as quality measurement parameter for image segmentation algorithms, in arXiv preprint arXiv:1605.07116 (2016)
Z. Wang et al., Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
T. Salimans et al., Improved techniques for training gans, in Advances in Neural Information Processing Systems (2016), pp. 2234–2242
C. Tan et al., A survey on deep transfer learning, in International Conference on Artificial Neural Networks (Springer, 2018), pp. 270–279
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Vamsi Kiran Reddy, P., Sajith Variyar, V.V. (2021). Image Enhancement Using GAN (A Re-Modeling of SR-GAN for Noise Reduction). In: Kaiser, M.S., Xie, J., Rathore, V.S. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2020). Lecture Notes in Networks and Systems, vol 190. Springer, Singapore. https://doi.org/10.1007/978-981-16-0882-7_64
Download citation
DOI: https://doi.org/10.1007/978-981-16-0882-7_64
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-0881-0
Online ISBN: 978-981-16-0882-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)