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arXiv:2611.08359 · v1

A Theoretical Analysis of a Bilevel Optimization Formulation for Combined Local-Global Optical Flow Models

首次提交 2026-11-06 01:38
版本更新 2026-11-06 01:38
雷达观测 2026-11-06 12:09
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摘要

This paper analyzes a bilevel formulation for learning the regularization parameter of combined local-global (CLG) optical flow models. The lower-level problem computes the flow from a Lucas--Kanade data term and a quadratic or total variation (TV) regularizer, and the upper-level problem selects the parameter that minimizes the squared distance to a ground-truth flow. The setting is finite-dimensional, and a full-rank assumption makes the lower-level solution unique. For the quadratic model we derive sufficient conditions for the existence of an optimal parameter, for every optimal parameter to be positive, and for strict local optimality. For TV regularization an optimal parameter always exists, because the lower-level solution vanishes for large parameters, and we give a sufficient condition for positivity that accounts for the coupling of the two flow components through the local structure tensors. Synthetic examples illustrate the conditions, including cases in which they fail.

作者备注:31 pages, 5 figures. Submitted to the Journal of Mathematical Imaging and Vision. Python code in the ancillary files

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基于摘要 · 非全文审阅

该文在有限维设定下对联合局部-全局(CLG)光流模型正则化参数学习的双层优化表述进行理论分析,给出二次与TV正则化下最优参数存在性、正性与严格局部最优性的充分条件。

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v1 · 2026-11-06 01:38 · 最新观测版本

A Theoretical Analysis of a Bilevel Optimization Formulation for Combined Local-Global Optical Flow Models

This paper analyzes a bilevel formulation for learning the regularization parameter of combined local-global (CLG) optical flow models. The lower-level problem computes the flow from a Lucas--Kanade data term and a quadratic or total variation (TV) regularizer, and the upper-level problem selects the parameter that minimizes the squared distance to a ground-truth flow. The setting is finite-dimensional, and a full-rank assumption makes the lower-level solution unique. For the quadratic model we derive sufficient conditions for the existence of an optimal parameter, for every optimal parameter to be positive, and for strict local optimality. For TV regularization an optimal parameter always exists, because the lower-level solution vanishes for large parameters, and we give a sufficient condition for positivity that accounts for the coupling of the two flow components through the local structure tensors. Synthetic examples illustrate the conditions, including cases in which they fail.

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