Hybrid deep learning model for image de-noising and de-mosaicking with adaptive Gannet optimization algorithm
Image reconstruction is a critical step in various applications, such as art restoration, medical image processing, and agriculture, but it faces challenges due to noise and mosaic artefacts. In this research, a novel approach is introduced for de-noising and de-mosaicking images to enhance image re...
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| Vydané v: | Network (Bristol) s. 1 - 27 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
England
23.07.2025
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| Predmet: | |
| ISSN: | 0954-898X, 1361-6536, 1361-6536 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | Image reconstruction is a critical step in various applications, such as art restoration, medical image processing, and agriculture, but it faces challenges due to noise and mosaic artefacts. In this research, a novel approach is introduced for de-noising and de-mosaicking images to enhance image reconstruction quality. The proposed model consists of three main steps: detail layer extraction, image de-noising using an Efficient Generative Adversarial Network (E-GAN), and de-mosaicking using an Adaptive Gannet-based Residual DenseNet (AG_DenseResNet). The publicly available Kodak dataset is utilized for the evaluation of the proposed model. The results show that the proposed outperforms conventional methods in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Squared Error (MSE), and Learned Perceptual Image Patch Similarity (LPIPS) and acquired the values of 53.93, 0.98, 2.76, and 0.23, respectively. |
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| Bibliografia: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 0954-898X 1361-6536 1361-6536 |
| DOI: | 10.1080/0954898X.2025.2529299 |