# BSAFusion: A Bidirectional Stepwise Feature Alignment Network for Unaligned Medical Image Fusion

arXiv: https://arxiv.org/abs/2412.08050 · HTML: https://mlsanity.com/paper/2412.08050/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 11 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion](https://mlsanity.com/paper/2211.14461/) (2022)
- [DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion](https://mlsanity.com/paper/2303.06840/) (2023)
- [Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registration](https://mlsanity.com/paper/2205.11876/) (2022)
- [Improving Misaligned Multi-modality Image Fusion with One-stage Progressive Dense Registration](https://mlsanity.com/paper/2308.11165/) (2023)

## Built on by (most cited first, 6 of 6)

- [A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarking](https://mlsanity.com/paper/2505.19858/) (2025)
- [MambaVF: State Space Model for Efficient Video Fusion](https://mlsanity.com/paper/2602.06017/) (2026)
- [Uncertainty-aware Spatial-Frequency Registration and Fusion for Infrared and Visible Images](https://mlsanity.com/paper/2605.13049/) (2026)
- [Pairwise Similarity Regularization for Semi-supervised Graph Medical Image Segmentation](https://mlsanity.com/paper/2503.12800/) (2025)
- [Learning Emergent Modular Representations in Multi-modality Medical Vision Foundation Models](https://mlsanity.com/paper/2605.21861/) (2026)
- [When Integral Meets Decomposition: A Signal-Level Self-Supervised Feature Decompose Paradigm for Multi-Modal Image Fusion](https://mlsanity.com/paper/2609.39004/) (2026)

## Abstract (arXiv)

If unaligned multimodal medical images can be simultaneously aligned and fused using a single-stage approach within a unified processing framework, it will not only achieve mutual promotion of dual tasks but also help reduce the complexity of the model. However, the design of this model faces the challenge of incompatible requirements for feature fusion and alignment; specifically, feature alignment requires consistency among corresponding features, whereas feature fusion requires the features to be complementary to each other. To address this challenge, this paper proposes an unaligned medical image fusion method called Bidirectional Stepwise Feature Alignment and Fusion (BSFA-F) strategy. To reduce the negative impact of modality differences on cross-modal feature matching, we incorporate the Modal Discrepancy-Free Feature Representation (MDF-FR) method into BSFA-F. MDF-FR utilizes a Modality Feature Representation Head (MFRH) to integrate the global information of the input image. By injecting the information contained in MFRH of the current image into other modality images, it effectively reduces the impact of modality differences on feature alignment while preserving the complementary information carried by different images. In terms of feature alignment, BSFA-F employs a bidirectional stepwise alignment deformation field prediction strategy based on the path independence of vector displacement between two points. This strategy solves the problem of large spans and inaccurate deformation field prediction in single-step alignment. Finally, Multi-Modal Feature Fusion block achieves the fusion of aligned features. The experimental results across multiple datasets demonstrate the effectiveness of our method. The source code is available at https://github.com/slrl123/BSAFusion.