A Lightweight Deep Learning Framework for Parallax-Tolerant Image Stitching
Image stitching aims to construct wide field-of-view scenes from multiple narrow-FoV images, yet existing deep learning-based approaches may introduce substantial computational and parameter overhead, limiting their applicability in efficiency-sensitive scenarios. To address this issue, we propose a lightweight deep stitching framework that integrates multi-scale feature fusion with attention-enhanced matching. Specifically, a transformer-based channel attention (TCA) block improves the discriminative capability of fused features in low-texture regions and enhances global consistency. A coordinate-aware correlation module (CACM) combines correlation-based matching with position-sensitive coordinate attention to support registration under parallax, while GhostNet serves as the shared backbone. On UDIS-D, the complete model achieves a 25.19 dB peak signal-to-noise ratio (PSNR) and a structural similarity index (SSIM) of 0.833 under the overlap-region protocol, with a reported full-system complexity of 19.28 giga multiply-accumulate operations (GMACs) and 52.86 M parameters. These results demonstrate a favorable accuracy–efficiency trade-off under the stated GMAC, runtime, and memory protocol and the potential of the proposed framework for resource-conscious image stitching applications.