arXiv Computer Vision
Translation-Invariant Tile-Based Phase Unwrapping with Residual-Weighted Multipath Averaging
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Sep 25
Adaptive Tiling for Least-Squares Phase Unwrapping: Runtime and Accuracy
The paper studies adaptive tiling strategies for least-squares phase unwrapping, comparing regular grids, quadtree, and kd-tree partitions. It evaluates nine criteria for quadtree subdivision, such as residue count and fringe density, across various tile sizes and budgets. Experiments show that while adaptive partitions use fewer tiles, they are slower than optimized grids and can reduce accuracy, indicating that partition construction and larger tile solving outweigh boundary savings.
arXiv Computer Vision
Sep 24
Adaptive double-phase Rudin--Osher--Fatemi denoising model
arXiv:2510.04382v3 Announce Type: replace-cross Abstract: Even though more than 30 years have passed since the seminal Rudin--Osher--Fatemi (ROF) paper on total variation (TV) denoising, it remains r...
arXiv Computer Vision
Sep 1
InfScene-SR: Seamless Super-Resolution of Arbitrarily Large Remote-Sensing Scenes via Variance-Preserving Joint Denoising
arXiv:2602.19736v3 Announce Type: replace Abstract: Diffusion models now give the best perceptual quality in super-resolution (SR), but their architecture and training confine them to small fixed cro...
arXiv Machine Learning
Sep 1
FiLM-GPNet: Geometry-Aware Pseudo-Supervised Phase Restoration with Zero-Shot Generalization for Large Temporal InSAR Stacks
arXiv:2608.29384v1 Announce Type: cross Abstract: The growing availability of dense commercial Synthetic Aperture Radar (SAR) time series enables temporal Interferometric SAR (InSAR) analysis, but fi...
arXiv AI
Aug 17
An InSAR Phase Unwrapping Framework for Large-scale and Complex Events
arXiv:2603. 21378v2 Announce Type: replace-cross Abstract: Phase unwrapping remains a critical and challenging problem in InSAR processing, particularly in scenarios involving complex deformation patterns.
arXiv Machine Learning
Jun 9
Phase Marginalization for Patch-Grid Instability in Vision Transformers
arXiv:2606. 08132v1 Announce Type: cross Abstract: Vision Transformers operate on fixed patch grids, which can introduce phase-dependent instability for dense prediction: changing the patch partition can change the token evidence available to a pixel, especially near boundaries.