Image Denoising Techniques for Cybersecurity and Forensic Applications
Hewa Majeed Zangana, Firas Mahmood Mustafa
Abstract
With the proliferation of digital evidence in cybersecurity and forensic investigations, image denoising has become essential for accurate analysis, where high-quality visuals are critical for identifying threats and verifying information. This chapter explores advanced AI-driven techniques for image denoising, emphasizing the role of deep learning, convolutional neural networks (CNNs), and generative models to enhance image clarity. By leveraging artificial intelligence, these methods adaptively reduce noise while preserving essential image features, improving both efficiency and reliability in digital forensic processes. Our examination includes a comparative analysis of traditional versus AI-based denoising approaches, assessing their applicability and effectiveness within cybersecurity and forensic environments. This chapter ultimately aims to provide a comprehensive overview of cutting-edge AI techniques that refine image quality, supporting better decision-making in complex, data-rich scenarios.