A Joint Framework of GFP-GAN and Real-ESRGAN for Real-World Image Restoration
Mousumi Hasan1, Nusrat Jahan Nishat2, Tanjina Rahman3, Mujiba Shaima4, Quazi Saad ul Mosaher5, Mohd. Eftay Khyrul Alam6
1Mousumi Hasan, Department of Computer Science and Engineering, Bangladesh Army International University of Science and Technology, Academic Block, Cumilla, Chittagong, Bangladesh.
2Nusrat Jahan Nishat, Department of Computer Science and Engineering, Bangladesh Army International University of Science and Technology, Academic Block, Cumilla, Chittagong, Bangladesh.
3Tanjina Rahman, Department of Computer Science and Engineering, Bangladesh Army International University of Science and Technology, Academic Block, Cumilla, Chittagong, Bangladesh.
4Mujiba Shaima, Department of Computer Science and Engineering, Bangladesh Army International University of Science and Technology, Academic Block, Cumilla, Chittagong, Bangladesh.
5Quazi Saad ul Mosaher, Department of Computer Science and Engineering, Bangladesh Army International University of Science and Technology, Academic Block, Cumilla, Chittagong, Bangladesh.
6Mohd. Eftay Khyrul Alam, Department of Computer Science and Engineering, Bangladesh Army International University of Science and Technology, Academic Block, Cumilla, Chittagong, Bangladesh.
Manuscript received on 30 December 2023 | Revised Manuscript received on 08 January 2024 | Manuscript Accepted on 15 January 2024 | Manuscript published on 30 January 2024 | PP: 32-42 | Volume-13 Issue-2, January 2024 | Retrieval Number: 100.1/ijitee.B979213020124 | DOI: 10.35940/ijitee.B9792.13020124
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the current era of digitalization, the restoration of old photos holds profound significance as it allows us to preserve and revive cherished memories. However, the limitations imposed by various websites offering photo restoration services prompted our research endeavor in the field of image restoration. Our motive originated from the personal desire to restore old photos, which often face constraints and restrictions on existing platforms. As individuals, we often encounter old and faded photographs that require restoration to revive the emotions and moments captured within them. The limits of existing photo restoration services prompted us to conduct this research, with the ultimate goal of contributing to the field of image restoration. To address this issue, we propose a joint framework that combines the Real-ESRGAN and GFP-GAN methods. Our recommended joint structure has been thoroughly tested on a broad range of severely degraded image datasets, and it has shown its efficiency in preserving fine details, recovering colors, and reducing artifacts. The research not only addresses the personal motive for restoring old photos but also has wider applications in preserving memories, cultural artifacts, and historical records through an effective and adaptable solution. Our deep learning-based approach, which leverages the synergistic capabilities of Real-ESRGAN and GFP-GAN, holds immense potential for revitalizing images that have suffered from severe degradation. This proposed framework opens up new avenues for restoring the visual integrity of invaluable historical images, thereby preserving precious memories for generations to come.
Keywords: GFP-GAN, Real-ESRGAN. Deep learning, Visual Integrity, Adaptable Solution.
Scope of the Article: Deep learning