In catastrophic environments such as fires, earthquakes, and explosions, images captured by drone cameras often become blurry because of strong vibrations. These vibrations severely affect the image quality and efficiency of emergency rescue operations. To address this issue, we propose an image deblurring method that uses unmanned aerial vehicle (UAV) inertial sensor data to construct the point spread function (PSF). The proposed method captures the motion information of an airborne camera using an inertial sensor and derives the PSF from this data, effectively overcoming the difficulties associated with traditional methods that consider complex textures, low contrast, or noise. The estimated PSF is combined with the total variation regularization technique to restore the images. By introducing the split Bregman iterative technique into the implemented algorithm, the complex optimization problem is effectively broken down into a series of simple sub-problems. This approach accelerates the calculation speed and yields high-precision image deblurring. Experimental and simulation results show that the proposed method effectively restores image blur caused by UAV vibrations, suppresses artifacts and ringing, and considerably improves the imaging quality of UAV cameras under vibration.