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 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.