Balaji Prabhu, BV and Narasipura, OSJ (2021) Improved Image Super-resolution Using Enhanced Generative Adversarial Network a Comparative Study. [Book Chapter]
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Super-resolution using generative adversarial networks is an approach for improving the quality of imaging system. With the advances in deep learning, convolutional neural networks-based models are becoming a favorite choice of researchers in image processing and analysis as it generates more accurate results compared to conventional methods. Recent works on image super-resolution have mainly focused on minimizing the mean squared reconstruction error and able to get high signal-to-noise ratios. But, they often lack high-frequency details and are not as accurate at producing high-resolution images as expected. With the aim of generating perceptually better images, this paper implements the enhanced generative adversarial model and compares with super-resolution generative adversarial model. The qualitative measures such as peak signal-to-noise ratio and structural similarity indices were used to assess the quality of the super-resolved images. The results obtained prove that, enhanced GAN model is able to recover more texture details when compared to super-resolution GAN models. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
| Item Type: | Book Chapter |
|---|---|
| Publication: | Lecture Notes on Data Engineering and Communications Technologies |
| Series.: | Lecture Notes on Data Engineering and Communications Technologies book series |
| Publisher: | Springer Science and Business Media Deutschland GmbH |
| Additional Information: | The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH |
| Keywords: | Convolutional neural networks; Deep learning; Image resolution; Optical resolving power; Signal to noise ratio; Textures, Adversarial networks; Conventional methods; High resolution image; High signal-to-noise ratio; Image processing and analysis; Image super resolutions; Peak signal to noise ratio; Structural similarity indices, Image enhancement |
| Department/Centre: | Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering) |
| Date Deposited: | 16 Aug 2021 08:56 |
| Last Modified: | 16 Aug 2021 08:56 |
| URI: | http://eprints.iisc.ac.in/id/eprint/69199 |
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