---
title: "Robust Pixel-wise Digital Image Correlation under Large Motions and Illumination Variations via a Global-to-Local Hybrid Neural Field"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.01018v1"
permalink: "https://feed.astralor.com/items/itm_d939d44c3efe54381b9eb00d"
published: "2026-10-31T15:00:50.000Z"
score: 56
channel: "tech"
tags: ["推理效率","Robust Pixel-wise Digital","Image Correlation","Large Motions","Illumination Variations","Global-to-Local Hybrid Neural","Field Digital","DIC"]
---

# Robust Pixel-wise Digital Image Correlation under Large Motions and Illumination Variations via a Global-to-Local Hybrid Neural Field

## 速览

Digital image correlation (DIC) is a widely used non-contact technique for full-field displacement measurement. Data-driven deep-learning DIC offers rapid inference but may generalize poorly to unseen speckle patterns, …

## 关键点

- Digital image correlation (DIC) is a widely used non-contact technique for full-field displacement measurement.
- Data-driven deep-learning DIC offers rapid inference but may generalize poorly to unseen speckle patterns, deformation modes, noise levels, and illumination conditions.
- Classical optimization-based DIC and recent image-constrained neural formulations avoid labeled displacement data, yet their convergence and accuracy remain vulnerable to initialization, large rigid-body motion, fine no…

## 全文

Digital image correlation (DIC) is a widely used non-contact technique for full-field displacement measurement. Data-driven deep-learning DIC offers rapid inference but may generalize poorly to unseen speckle patterns, deformation modes, noise levels, and illumination conditions. Classical optimization-based DIC and recent image-constrained neural formulations avoid labeled displacement data, yet their convergence and accuracy remain vulnerable to initialization, large rigid-body motion, fine non-uniform deformation, and gray-value disturbances. This paper presents a global-to-local hybrid neural DIC framework for robust pixelwise measurement. Stage 1 estimates a dominant in-plane rigid transformation through coarse rotation search, phase correlation, and masked refinement, providing a reliable initialization under large translation and rotation. Stage 2 represents the residual displacement with a hybrid Kolmogorov-Arnold network (KAN) and multilayer perceptron (MLP), and optimizes it using a valid-overlap, illumination-robust structural loss. The local gray-value model explicitly accommodates multiplicative and additive intensity changes. Simulated and real experiments demonstrate accurate and stable displacement recovery under noise, non-uniform deformation, large rotation, illumination variation, and degraded imaging conditions.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_d939d44c3efe54381b9eb00d