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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Bibliographic Details
Published in:Network (Bristol) pp. 1 - 27
Main Authors: K, John Peter, S.R, SylajaVallee Narayan, N, Muthuvairavan Pillai, S.P, Predeep Kumar
Format: Journal Article
Language:English
Published: England 23.07.2025
Subjects:
De-noising
deep learning
de-mosaicking
image reconstruction
generative adversarial learning
ISSN:0954-898X, 1361-6536, 1361-6536
Online Access:Get full text
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Summary: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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ISSN:0954-898X
1361-6536
1361-6536
DOI:10.1080/0954898X.2025.2529299