###### Abstract  
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 \(\times\) 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. w +
Footnote †: journal: Applied sciences  
article  
[color=gray! networks and high-performance computing resources, making them unsuitable for deployment in resource-constrained environments where low latency and real-time performance are critical [8]. These models often perform poorly on edge platforms such as smart surveillance systems and mobile sensing devices. Meanwhile, traditional image rectification and stitching methods are primarily based on geometric modeling--such as polynomial distortion correction and homography transformations [9; 10]--which, despite their adaptability in specific scenarios, lack generalizability and robustness when applied to high-resolution images, non-rigid deformations, or dynamic scenes [11]. Furthermore, multi-camera stitching tasks face additional system-level challenges such as image synchronization, dynamic seam-line adjustment, frame rate control, and resource allocation [12], posing significant trade-offs between lightweight design, high performance, and deployment feasibility. In response to the aforementioned challenges, this study proposes an image processing framework that achieves both high-precision distortion correction and efficient deployment on edge devices. At the algorithmic level, a lightweight image rectification network is designed, integrating a Swin Transformer-based encoder architecture, a Thin Plate Spline (TPS) control point prediction module, and an optical flow estimation mechanism [13; 14; 15]. The network enhances feature representation through a sliding window attention mechanism and multi-scale feature extraction modules while leveraging optical flow vector directions as structural constraints to effectively model nonlinear distortions, thereby improving rectification accuracy and robustness. To further enhance generalization performance, the framework incorporates a comprehensive data augmentation strategy--including random cropping, elastic deformation, illumination simulation, and noise injection [16]--as well as a spatial attention module (SAM) and multi-branch feature fusion [17], enabling the model to better adapt to complex scene variations. At the system engineering level, a real-time dual-camera image stitching system is implemented on the Jetson TX2 NX edge computing platform. Leveraging CUDA-based parallel computation and the DeepStream multimedia framework, the system realizes an end-to-end pipeline encompassing image acquisition, preprocessing, rectification, stitching, and video streaming [18; 19].