Supervised Denoising

Supervised denoising aims to train models to remove noise from data, typically images, by learning from paired noisy and clean examples. Recent research focuses on improving the accuracy and efficiency of these models, exploring architectures like convolutional neural networks and diffusion models, as well as novel training strategies such as those incorporating generative components or specific target guidance to enhance detail preservation and reduce computational cost. These advancements are significant because they enable improved image quality in various applications, such as medical imaging and computer vision, and offer potential for reducing the need for extensive data acquisition. Furthermore, theoretical work is increasingly focusing on understanding the behavior of these models under real-world conditions, such as distribution shifts and low-rank data.