Spatial Angular Correlation

Spatial angular correlation focuses on leveraging the relationships between different viewpoints and spatial locations within light field data, primarily to improve image resolution and quality. Current research emphasizes efficient algorithms, such as transformer networks and novel architectures like Mamba, to effectively model these complex, often non-local, correlations in high-dimensional data, overcoming limitations of previous convolutional neural network approaches. This work is significant for advancing light field imaging applications, particularly in areas like super-resolution, where improved efficiency and accuracy are crucial for processing large datasets and achieving high-fidelity results.