In visual SLAM algorithms, the assumption of scene rigidity serves as the foundation for algorithm operation. However, this assumption restricts the use of most visual SLAM systems in densely populated or vehicle-intensive environments, limiting their application in scenarios involving service robots or autonomous driving. In this article, we introduce IVI-SLAM, which is a visual SLAM system based on DynaSLAM. IVI-SLAM extends DynaSLAM by adding background restoration capabilities for both monocular and stereo modes, along with a method for depth recovery. We detect dynamic objects by using deep learning techniques and restore the background occluded by these dynamic objects by iterative training of image inpainting models. Subsequently, we utilize a depth recovery approach to restore the depth values in the affected region. We evaluate our system on publicly available monocular and stereo datasets, achieving promising results.