Spatial Coherence

Spatial coherence, the consistent relationship between neighboring data points in space and/or time, is a crucial concept across diverse scientific fields, aiming to improve the accuracy and efficiency of data processing and model generation. Current research focuses on incorporating spatial coherence into various model architectures, including diffusion models, neural networks (both diffractive and conventional), and variational autoencoders, often through novel loss functions or data augmentation techniques that explicitly model or enforce this consistency. This work has significant implications for improving the quality and robustness of applications ranging from video generation and image reconstruction to object detection and motion tracking, particularly in challenging scenarios with noise or occlusions. The development of more efficient and effective methods for leveraging spatial coherence promises to advance numerous fields.