To solve the problems of aberrations and motion blur encountered in infrared imaging, an algorithm for infrared imaging deblurring based on spectrum analysis was proposed. By analyzing the blur problems, a blur function estimation approach was constructed by using the spectral information of infrared images, and the deblurring of infrared images was achieved by optimizing the data fitting terms and regularization terms. The experiments were conducted on datasets containing motion-blurred images and static aberration images. The results show that in the experiments of datasets deblurring, in comparison to previous methods, the peak signal-to-noise ratio (PSNR) of proposed algorithm is improved by 2.01 dB, and the structural similarity index (SSIM) is improved by 0.06. The static aberration images are acquired using a short-wave infrared detector under laboratory conditions, and the restored images show the significant improvement subjectively, particularly in high-throughput optical system imaging scenarios.