Frequency Decomposition

Frequency decomposition, the separation of signals or images into constituent frequency components, is a powerful tool used to improve various machine learning and signal processing tasks. Current research focuses on leveraging this technique for enhanced regularization in neural networks, improved image restoration and enhancement by treating high and low frequencies differently, and enabling more efficient level-of-detail processing in 3D representations like neural radiance fields (NeRFs). These advancements are leading to improved performance in image classification, object detection, audio processing, and lossless image compression, demonstrating the broad applicability and significance of frequency decomposition across diverse fields.