Learning Based Denoising

Learning-based denoising aims to improve the quality of noisy data, such as images and sensor readings, by leveraging the power of deep learning models. Current research focuses on developing self-supervised and unsupervised methods that avoid the need for large, paired datasets of clean and noisy data, employing architectures like transformers and convolutional neural networks, and incorporating techniques like noise prior estimation and physics-informed constraints. These advancements are significant because they enable improved performance in various applications, including medical imaging, audio processing, and remote sensing, where obtaining clean ground truth data is often difficult or impossible. The development of more robust and efficient denoising methods is crucial for enhancing the accuracy and reliability of numerous scientific instruments and technologies.