Phase Prediction

Phase prediction, the task of estimating the phase component of a signal given its amplitude, is a crucial area of research across diverse fields, aiming to improve signal reconstruction and analysis. Current research focuses on developing efficient and accurate neural network architectures, often employing convolutional networks and incorporating techniques like adversarial training and multi-stage prediction to enhance performance. These advancements are impacting various applications, including speech synthesis, medical image analysis (e.g., CT scan interpretation and surgical video analysis), and communication systems (e.g., reconfigurable intelligent surfaces), where precise phase information is essential for improved quality and efficiency. The development of robust and low-latency phase prediction methods is driving progress in these fields.