Perceptual Aliasing

Perceptual aliasing, the distortion caused by insufficient sampling resolution, is a significant challenge across various fields, from image and video processing to audio coding and neural network design. Current research focuses on developing anti-aliasing techniques, employing methods like diffusion models, frequency-domain representations (e.g., frequency-specific filtering), and novel pooling strategies in convolutional neural networks to mitigate these distortions. Addressing aliasing improves the accuracy and robustness of models in diverse applications, ranging from 3D scene rendering and speech synthesis to semantic segmentation and robust machine learning. The development of alias-free architectures and algorithms is crucial for enhancing the fidelity and reliability of numerous signal processing and machine learning systems.