Toward Reliable Homography Estimation Under Adverse Degradations: An Optimization-Driven Approach

Homography estimation is a fundamental problem in geometric computer vision, yet reliable estimation remains highly challenging under adverse imaging conditions. In low-light, haze, rain, underwater, dynamic, and cross-modality scenarios, degradation and appearance discrepancy severely weaken feature correlations, making both hand-crafted matching pipelines and purely data-driven regression models prone to unstable correspondences. To address this limitation, we propose an optimization-driven homography estimation framework that reformulates robust alignment as a progressive energy minimization process. The proposed framework consists of complementary components with explicit optimization roles. First, a Robust Feature Initialization (RFI) strategy provides a static-plane-aware warm start by suppressing unreliable dynamic or degradation-corrupted regions. Second, an Alignment Fidelity Module (AFM) unfolds fidelity minimization into learnable residual update steps, progressively refining the deformation field through feature-space alignment. Third, a Perception Regularization Module (PRM) introduces task-driven semantic constraints during training, encouraging the learned deformation to preserve both geometric consistency and perception-level reliability. In addition, we develop a Degradation Constraint Learning (DCL) strategy, where an auxiliary restoration decoder encourages the shared encoder to learn degradation-robust intermediate representations. To facilitate evaluation under challenging conditions, we construct a comprehensive harsh-environment homography benchmark covering synthetic and real-world scenes. Extensive experiments demonstrate that the proposed method achieves state-of-the-art alignment accuracy, robust generalization across diverse degradations, and consistent improvements on five downstream perception tasks, while maintaining practical inference efficiency with an adaptive stopping criterion.