Fast Fourier Convolution

Fast Fourier Convolution (FFC) is a neural network operator designed to efficiently capture both local and global information within data, addressing limitations of standard convolutions in handling long-range dependencies. Current research focuses on applying FFCs in diverse areas, including image and video processing (e.g., super-resolution, inpainting, dehazing), audio processing (e.g., upsampling, enhancement), and medical imaging (e.g., MRI reconstruction), often integrated into architectures like Swin Transformers or U-Nets. The effectiveness of FFCs in improving model performance across various tasks, particularly those involving long sequences or large receptive fields, highlights their significance for advancing deep learning applications.