Recent Research Advances in Image Denoising

Abstract

Image denoising is a key area in image processing, especially important in applications such as medical imaging and remote sensing as the demand for high-quality images grows. This paper focuses on the latest research on deep learning-based denoising techniques, including Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Swin Transformer, and explores their principles, architectures, and effects of removing complex noise while maintaining image details. Deep learning models are capable of learning complex image features and adapting to different noise patterns and perform well in CT image and synthetic aperture radar (SAR) image denoising. Deep learning models have significant advantages over traditional methods in dealing with complex noise and preserving details. However, these methods still face challenges such as high computational requirements, dependence on large-scale datasets, and difficulty in adapting to realistic noise. This paper also discusses possible solutions to cope with these problems by creating real-world noisy datasets, optimizing model architectures and reducing computational costs, and looks at future research directions.