Image Reconstruction and De-noising using Neural Network: A Systematic Literature Review
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Abstract
Image reconstruction and de-noising are critical components in modern image processing systems, with significant applications in medical imaging, satellite photography, surveillance, and autonomous systems. Traditional signal processing approaches, while effective to an extent, often struggle with complex noise patterns and high-detail preservation. In recent years, neural network-based methods have emerged as powerful tools to address these challenges, offering superior performance in both noise reduction and image structure restoration. This systematic literature review provides a comprehensive overview of recent advancements in neural network-based techniques for image reconstruction and de-noising. The reviewed literature is categorized into key neural architectures such as Convolutional Neural Networks (CNNs), Autoencoders, Generative Adversarial Networks (GANs), and Recurrent Neural Networks (RNNs). Each approach is evaluated in terms of methodology, dataset usage, performance metrics (PSNR, SSIM, MSE), and application domain. Despite these advancements, challenges remain in areas such as generalization to unknown noise types, model interpretability, training stability, and real-time deployment on low-power devices. This review also highlights future research opportunities, including hybrid neural models, physics-informed learning, federated training, and self-supervised approaches. Overall, this work aims to serve as a valuable resource for researchers and practitioners seeking to understand and develop state-of-the-art neural network models for robust image de-noising and reconstruction tasks.
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