# Image Alignment Based on Deep Learning to Extract Deep Feature Information from Images

## TL;DR

This paper introduces a deep learning network for image alignment that improves accuracy by extracting deep semantic features.

## Contribution

The novel DFA-Net uses spatial pyramid pooling and self-attention to enhance feature adaptability and robustness in image alignment.

## Key findings

- DFA-Net reduces RMSE metrics by 0.661 and 0.473 on MSRS and RoadScene datasets.
- SSIM, MI, and NCC metrics improve significantly compared to benchmark models.
- Visualizations confirm better feature quality and stability of the proposed method.

## Abstract

To overcome the limitations of traditional image alignment methods in capturing deep semantic features, a deep feature information image alignment network (DFA-Net) is proposed. This network aims to enhance image alignment performance through multi-level feature learning. DFA-Net is based on the deep residual architecture and introduces spatial pyramid pooling to achieve cross-scalar feature fusion, effectively enhancing the feature’s adaptability to scale. A feature enhancement module based on the self-attention mechanism is designed, with key features that exhibit geometric invariance and high discriminative power, achieved through a dynamic weight allocation strategy. This improves the network’s robustness to multimodal image deformation. Experiments on two public datasets, MSRS and RoadScene, show that the method performs well in terms of alignment accuracy, with the RMSE metrics being reduced by 0.661 and 0.473, and the SSIM, MI, and NCC improved by 0.155, 0.163, and 0.211; and 0.108, 0.226, and 0.114, respectively, compared with the benchmark model. The visualization results validate the significant improvement in the features’ visual quality and confirm the method’s advantages in terms of stability and discriminative properties of deep feature extraction.