Phase Difference Estimation

Phase difference estimation focuses on accurately determining the phase shift between signals or components within a signal, crucial for various applications requiring precise temporal or spatial alignment. Current research emphasizes the use of deep neural networks, particularly convolutional neural networks and transformers, often within a framework of decoupled magnitude and phase estimation, to improve accuracy and efficiency. These advancements are impacting diverse fields, from audio signal processing (improving virtual effects and speech enhancement) to biomedical imaging (enhancing light propagation modeling) by enabling more precise measurements and improved signal reconstruction. The development of robust and efficient algorithms is driving significant improvements in the accuracy and speed of phase estimation across multiple scientific domains.