Improving Misaligned Multi-modality Image Fusion with One-stage Progressive Dense Registration

TL;DR

This paper introduces a novel one-stage progressive dense registration method that improves multi-modality image fusion by accurately aligning images in a single optimization process, enhancing fusion quality.

Contribution

The paper proposes a unified one-stage registration framework with dense deformation fusion and progressive feature refinement, outperforming traditional two-stage methods.

Findings

Outperforms existing methods in misaligned multi-modality image fusion

Achieves more accurate registration with a single optimization process

Produces higher quality fused images with better structural preservation

Abstract

Misalignments between multi-modality images pose challenges in image fusion, manifesting as structural distortions and edge ghosts. Existing efforts commonly resort to registering first and fusing later, typically employing two cascaded stages for registration,i.e., coarse registration and fine registration. Both stages directly estimate the respective target deformation fields. In this paper, we argue that the separated two-stage registration is not compact, and the direct estimation of the target deformation fields is not accurate enough. To address these challenges, we propose a Cross-modality Multi-scale Progressive Dense Registration (C-MPDR) scheme, which accomplishes the coarse-to-fine registration exclusively using a one-stage optimization, thus improving the fusion performance of misaligned multi-modality images. Specifically, two pivotal components are involved, a dense Deformation Field Fusion (DFF) module and a Progressive Feature Fine (PFF) module. The DFF aggregates the predicted multi-scale deformation sub-fields at the current scale, while the PFF progressively refines the remaining misaligned features. Both work together to accurately estimate the final deformation fields. In addition, we develop a Transformer-Conv-based Fusion (TCF) subnetwork that considers local and long-range feature dependencies, allowing us to capture more informative features from the registered infrared and visible images for the generation of high-quality fused images. Extensive experimental analysis demonstrates the superiority of the proposed method in the fusion of misaligned cross-modality images.