INNOVATIVE APPROACHES IN APPLIED MATHEMATICS FOR ENHANCED IMAGE DENOISING AND NOISE REDUCTION
Abstract
Image denoising and noise reduction are pivotal processes in various domains, including medical imaging, remote sensing, and digital photography. Recent advances in applied mathematics have notably enhanced the capabilities in this field. This review paper delves into contemporary developments and methodologies for image denoising and noise reduction, rooted in applied mathematics. It provides an extensive survey of both traditional and cutting-edge approaches, emphasizing their theoretical underpinnings, computational strategies, and practical applications. Special focus is given to innovative algorithms and mathematical models that exploit advanced concepts such as optimization, statistical inference, deep learning, and sparse representations. Furthermore, the paper addresses existing challenges, future directions, and emerging trends, offering researchers and practitioners a thorough understanding and insights into the progressive techniques shaping the future of image denoising and noise reduction.
Keywords
Image denoising, Noise reduction, Optimization techniques, Machine learning, Deep learning, Convolutional neural networks (CNNs).
