Signal Representation

Signal representation research focuses on efficiently and effectively encoding signals—such as images, audio, and sensor data—using mathematical and computational methods. Current efforts center on implicit neural representations (INRs), employing neural networks to map coordinates to signal attributes, with variations like convolutional INRs and those incorporating semantic priors to improve performance and address limitations in handling high-frequency components and noise. These advancements aim to improve signal compression, reconstruction, and manipulation, impacting fields ranging from image processing and medical imaging to communication systems and biological signal analysis.