Remote Sensing Image Enhancement And Denoising Using Deep Learning (Cnn)

Abstract

Remote sensing images often suffer from noise and degradation, which can hinder subsequent analysis and interpretation. In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown promising results in various image processing tasks. This paper presents a comprehensive review of deep learning-based approaches for remote sensing image enhancement and denoising. We discuss the challenges associated with remote sensing image processing, explore the fundamentals of CNNs, and delve into the methodologies used for image enhancement and denoising. Additionally, we highlight prominent deep learning architectures and techniques employed for this purpose, along with their advantages and limitations. Furthermore, we provide insights into the potential applications and future directions in this rapidly evolving field.