Proposes a psychovisual-inspired deep learning method that encodes images in learned frequency sub-bands for interpretable semantic structures and reduced depth dependence.
arXiv preprint arXiv:2508.01975
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
High-dimensional embedding prior improves diffusion-based k-space MRI reconstruction under noise by augmenting representation space.
citing papers explorer
-
Deep Psychovisual Image Representations
Proposes a psychovisual-inspired deep learning method that encodes images in learned frequency sub-bands for interpretable semantic structures and reduced depth dependence.
-
High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
High-dimensional embedding prior improves diffusion-based k-space MRI reconstruction under noise by augmenting representation space.