Wide-Angle Image Distortion Correction and Embedded Stitching System Design Based on Swin Transformer
Shiwen Lai, Zuling Cheng, Wencui Zhang, Maowei Chen · Applied Sciences · 2025
Wide-angle images often suffer from severe radial distortion, compromising geometric accuracy and challenging image correction and real-time stitching, especially in resource-constrained embedded environments. To address this, this study proposes a wide-angle image correction and stitching framework based on a Swin Transformer, optimized for lightweight deployment on edge devices. The model integrates multi-scale feature extraction, Thin Plate Spline (TPS) control point prediction, and optical flow-guided constraints, balancing correction accuracy and computational efficiency. Experiments on synthetic and real-world datasets show that the method outperforms mainstream algorithms, with PSNR gains of 3.28 dB and 2.18 dB on wide-angle and fisheye images, respectively, while maintaining real-time performance. To validate practical applicability, the model is deployed on a Jetson TX2 NX device, and a real-time dual-camera stitching system is built using C++ and DeepStream. The system achieves 15 FPS at 1400 × 1400 resolution, with a correction latency of 56 ms and stitching latency of 15 ms, demonstrating efficient hardware utilization and stable performance. This study presents a deployable, scalable, and edge-compatible solution for wide-angle image correction and real-time stitching, offering practical value for applications such as smart surveillance, autonomous driving, and industrial inspection.