Frequency Loss

Frequency loss, a concept increasingly used in image and signal processing, aims to improve the accuracy and robustness of models by incorporating frequency domain information into the loss function during training. Current research focuses on developing novel loss functions that leverage different frequency representations (e.g., Fourier transforms, Laplacian pyramids) and integrating them with various architectures, including multi-scale and U-shaped networks, and Generative Adversarial Networks (GANs). This approach addresses limitations of traditional spatial-domain losses, particularly in handling misaligned data or generating high-frequency details, leading to improved performance metrics (e.g., PSNR, SSIM) in tasks like image restoration, enhancement, and speech enhancement. The resulting advancements have significant implications for various applications requiring high-fidelity image and signal processing.