A learnable nonlocal block that mimics classical neighbor matching and collaborative filtering on multiscale features produces competitive RAW denoising with far fewer parameters than current deep models and generalizes across sensors.
and Krivitzky, Eric M
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A framework uses offline-paired LR/HR data and POD latent-space linear models with Kalman filtering to reconstruct high-resolution velocity fields from coarse real-time event-based velocimetry, outperforming cubic interpolation on turbulent jet and ribbed-channel flows.
citing papers explorer
-
Learned Nonlocal Feature Matching and Filtering for RAW Image Denoising
A learnable nonlocal block that mimics classical neighbor matching and collaborative filtering on multiscale features produces competitive RAW denoising with far fewer parameters than current deep models and generalizes across sensors.
-
Real-Time Estimation of High-Resolution Flow Fields and Reduced-Order Coordinates from Event-Based Imaging Velocimetry
A framework uses offline-paired LR/HR data and POD latent-space linear models with Kalman filtering to reconstruct high-resolution velocity fields from coarse real-time event-based velocimetry, outperforming cubic interpolation on turbulent jet and ribbed-channel flows.