Speckle Reduction

Speckle reduction aims to remove granular noise from images produced by coherent imaging techniques like ultrasound, SAR, and OCT, improving image quality and diagnostic accuracy. Current research heavily emphasizes deep learning approaches, including convolutional neural networks, autoencoders (with and without skip connections), and scattering networks, often employing unsupervised or self-supervised training methods to overcome the limitations of requiring clean ground truth data. These advancements are significantly impacting various fields, enhancing the interpretability of medical images, improving the accuracy of remote sensing data analysis, and enabling more efficient processing of large datasets in real-time applications.