Image stitching is widely used in panoramic image generation and mobile visual processing. However, during image capture with handheld devices, variations in camera pose and orientation often lead to inconsistent perspectives among images. This frequently results in distortions and misalignments in the stitched result. Most existing methods rely solely on visual information for image registration and stitching. They are not only computationally expensive, but also prone to geometric distortions when significant changes in camera pose occur. To address this, we propose an IMU-Accelerated Image Stitching method, named IAIS. IAIS leverages the built-in IMU of the camera to compute rotational and translational information between images, which is then used as a prior for image registration and stitching. This effectively alleviates image distortions caused by variations in camera pose. Based on the rotation and translation information, we estimate the overlapping region between the images to be stitched. Feature point extraction and matching are performed only within this region, thus significantly accelerating the registration process. Furthermore, we select a global transformation that closely aligns with the rotation angle and combine it with grid-based local homographies to enhance stitching quality and continuity. We have implemented a prototype system of IAIS and performed sufficient evaluations. The results demonstrate that our method improves stitching speed while maintaining high accuracy, and achieves superior visual quality compared to existing approaches.