Joint Denoising

Joint denoising research focuses on simultaneously removing noise and performing another image or signal processing task, such as classification, reconstruction, or enhancement, to improve overall data quality and downstream analysis. Current efforts utilize deep learning models, often employing variations of U-Nets, Transformers, and GANs, to integrate these tasks within a single framework, achieving superior performance compared to sequential processing. This approach is proving valuable across diverse applications, including medical imaging (e.g., MRI, SPECT), remote sensing (SAR), microscopy, and speech enhancement, by improving data quality and efficiency.