Real-Time Image Denoising Using Deep Learning for Cybersecurity Applications
Nada Mohammed Murad, Sulaiman Yousif Hardan, Saadaldeen Rashid Ahmed, L. Mubark Ali, Ravi Sekhar, Pritesh Shah, B. S. Veena
Abstract
Real-world cybersecurity applications often encounter challenges posed by noisy images, necessitating effective denoising techniques for accurate analysis and decision-making. Despite previous studies in image denoising, gaps remain in addressing the specific requirements of cybersecurity scenarios. In this study, we propose a novel approach for real-time image denoising using deep learning tailored for cybersecurity applications. Our algorithm leverages advanced deep learning architectures to efficiently denoise images while preserving critical information. We curated a diverse dataset comprising noisy images representative of real-world cybersecurity scenarios to train and evaluate our model. Through rigorous experimentation, we achieved a high accuracy of 92%, showcasing the efficacy of our approach in accurately classifying pixels as noise or signal. Our aim is to bridge the gap between image denoising techniques and cybersecurity requirements, providing a reliable solution for enhancing security measures in real-world applications.