<![CDATA[In modern defense applications, the accuracy and clarity of digital images are crucial, especially for tasks like surveillance, reconnaissance, and intelligence gathering. However, noise introduced during image acquisition or transmission significantly degrades image quality. This paper presents a comprehensive review of various noise removal techniques employed in digital image processing for defense systems. The review focuses on both linear and non-linear methods, including matrix decomposition, hybrid deep learning, Generative Adversarial Networks (GANs), and trimming filters. Emphasis is placed on the effectiveness of each technique in enhancing image quality while preserving critical details. The use of linear and non-linear methods such as deep learning-based approaches is shown to outperform traditional linear filters in handling complex noise patterns, particularly in scenarios requiring precise object detection and image restoration. The paper highlights a comprehensive overview of the researched literature and shows the latest trends and developments in the field. Finally, recommendations for future research and the development of more robust noise reduction methods are provided, aiming to improve operational effectiveness in defense applications.]]>
