Image Recovery

Image recovery aims to reconstruct high-quality images from incomplete, noisy, or otherwise degraded data, a crucial task across numerous scientific and engineering fields. Current research emphasizes developing robust algorithms that handle various types of image corruption, including noise, blur, and missing data, often leveraging deep learning architectures like convolutional neural networks and transformers, along with advanced optimization techniques such as plug-and-play methods and iterative reweighted least squares. These advancements improve the accuracy and efficiency of image reconstruction, impacting diverse applications such as medical imaging, microscopy, and remote sensing by enabling higher-resolution imaging and more reliable data analysis.