Recent advancements suggest that employing small Graph Neural Networks to craft preconditioners could significantly enhance performance while preserving necessary sparsity, thereby optimizing computational efficacy.
Although our Transformer-based deep learning model provides state-of-the-art performance in both resolution enhancement and noise reduction for moderate noise levels, restoration becomes impossible when the noise level exceeds a threshold.
The new LieBN framework provides a systematic way to extend batch normalization to Riemannian manifolds, specifically catering to SPD (symmetric positive definite) matrices.
