Translation-Invariant Tile-Based Phase Unwrapping with Residual-Weighted Multipath Averaging
- URL: http://arxiv.org/abs/2609.13409v1
- Date: Fri, 11 Sep 2026 18:17:51 GMT
- Title: Translation-Invariant Tile-Based Phase Unwrapping with Residual-Weighted Multipath Averaging
- Abstract summary: Phase unwrapping is a key step in interferometric and coherent imaging.<n>We propose a strategy in which every tile is unwrapped in the frequency domain, through the Discrete Cosine Transform (DCT) and the least squares (LS) formalism of Ghiglia et al.<n>Unwrapping and noise filtering are performed jointly, which regularizes an otherwise ill-posed inverse problem.
- Score: 2.349064979059293
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Phase unwrapping is a key step in interferometric and coherent imaging, where the physical quantity of interest is carried by a phase that the instrument delivers only modulo 2*pi. The difficulty in two dimensions is to separate the jumps caused by wrapping from those produced by noise, by true discontinuities, by under-sampling or by decorrelation. Spatial-domain and frequency-domain methods have both been studied extensively, each with advantages the other lacks; hybrid schemes combining the two remain scarce. We propose a semi-global tile-based strategy in which every tile is unwrapped in the frequency domain, through the Discrete Cosine Transform (DCT) and the least squares (LS) formalism of Ghiglia et al., the tiles being merged spatially. Unwrapping and noise filtering are performed jointly, which regularizes an otherwise ill-posed inverse problem. An error stays confined to the tile in which it arose; the tiling artifacts are removed by averaging over every shift of the grid and over the symmetries of the square; and that average is weighted by the Poisson residual each pass leaves behind, so that a pass whose tile boundaries fell on a discontinuity does not impose its seam on the result. Experiments on synthetic and real data, against four reference algorithms from four distinct families and under six complementary metrics, show that the proposed method matches or improves on the state of the art. One of these metrics, a corrected cyclic re-wrap residual introduced here, needs no ground truth and therefore remains available on real acquisitions.
Related papers
- A mesh-free multiresolution deep energy method with phase-field modeling of brittle fracture [3.177203247615157]
Phase-field modeling of brittle fracture removes the need to track cracks explicitly.<n>We propose a mesh-free discretization in which a single neural network represents the displacement and phase fields.
arXiv Detail & Related papers (2026-08-25T06:36:14Z) - CoCurve: Cross-Module Co-Pruning Curvature for Training-Free Structured LLM Pruning [47.406985340436925]
CoCurve is a calibration-only, fine-tuning-free method that prunes attention and FFN units jointly.<n>It reduces to one budgeted quadratic program, solved in a single shot under a shared attention--FFN budget, with no labels, fine-tuning, or recovery.
arXiv Detail & Related papers (2026-07-20T05:27:57Z) - Certified boundary-magic witness for state-dependent proto-area in a holographic code [0.0]
We study a four-qubit product-EPR holographic code whose reconstructing region contains matter and one leg of a geometry bond.<n>We show that only matter-controlled bond motion produces a leading quadratic logical-state dependence of the boundary entropy.
arXiv Detail & Related papers (2026-07-12T09:16:46Z) - Wavelet Phase Diffusion for Structurally and Semantically Consistent Sim-to-Real Translation [8.622300381959768]
Simulation-to-reality translation must bridge the appearance gap between synthetic and real domains.<n>We introduce Wavelet Phase Diffusion, which enables spatially adaptive phase injection without global spectral interference.<n>Ours outperforms prior methods in realism and semantic consistency while maintaining competitive structural alignment.
arXiv Detail & Related papers (2026-07-08T13:30:26Z) - Closed-Form Spectral Regularization for Multi-Task Model Merging [96.82449201305234]
Model merging combines several independently fine-tuned experts into a single multi-task model without any training data.<n>State-of-the-art merging methods formulate merging as a layer-wise interference problem.<n>We propose SWUDI, a closed-form method that combines a soft exponential filter, which matches the gradient-flow trajectory of iterative descent.
arXiv Detail & Related papers (2026-06-05T14:00:47Z) - GeoFlowVLM: Geometry-Aware Joint Uncertainty for Frozen Vision-Language Embedding [3.0708725114491293]
We propose textbfGeoFlowVLM as a post-hoc adapter that learns the joint distribution of paired $ell$-normalised dual-encoder VLM embeddings.<n>A consistency result shows that, in the population limit, the trained network exposes the joint flow and both cross-modal conditional flows.<n>We derive two quantities from this single model: a conditional retrieval entropy that quantifies aleatoric ambiguity with a decision-theoretic interpretation via a Fano-type bound, and a marginaltypicality score justified by an exact chain-rule decomposition of the joint NLL
arXiv Detail & Related papers (2026-05-13T11:12:18Z) - Dual-space posterior sampling for Bayesian inference in constrained inverse problems [5.739708455652395]
Inverse problems constrained by partial differential equations are often ill-conditioned due to noisy and incomplete data or inherent non-uniqueness.<n>We develop a new technique for translating hard physical constraints, such as the wave equation, into prior distributions amenable to existing sampling techniques.
arXiv Detail & Related papers (2026-02-28T00:34:31Z) - Clustered Switchback Designs for Experimentation Under Spatio-temporal Interference [44.644520116360106]
We estimate the global average treatment effect (GATE), the difference between average outcomes having exposed all units at all times to treatment or to control.<n>We propose a clustered switchback design, where units are grouped into clusters and time steps are grouped into blocks.<n>We show that for graphs that admit good clustering, a truncated Horvitz-Thompson estimator achieves a $tilde O(1/NT)$ mean squared error (MSE)<n>Our results simultaneously generalize the results from citethu2022switchback,ugander2013graph and citetleung2022rate
arXiv Detail & Related papers (2023-12-25T01:00:58Z) - Generalization Bounds for Inductive Matrix Completion in Low-noise
Settings [46.82705757568271]
We study inductive matrix completion (matrix completion with side information) under an i.i.d. subgaussian noise assumption.
We obtain for the first time generalization bounds with the following three properties.
arXiv Detail & Related papers (2022-12-16T08:30:41Z) - Composed Image Retrieval with Text Feedback via Multi-grained
Uncertainty Regularization [73.04187954213471]
We introduce a unified learning approach to simultaneously modeling the coarse- and fine-grained retrieval.
The proposed method has achieved +4.03%, +3.38%, and +2.40% Recall@50 accuracy over a strong baseline.
arXiv Detail & Related papers (2022-11-14T14:25:40Z) - Lifting the Convex Conjugate in Lagrangian Relaxations: A Tractable
Approach for Continuous Markov Random Fields [53.31927549039624]
We show that a piecewise discretization preserves better contrast from existing discretization problems.
We apply this theory to the problem of matching two images.
arXiv Detail & Related papers (2021-07-13T12:31:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.