Camera gimbal systems pervade various applications such as navigation, target tracking, security and surveillance. The need for higher steering rate (rotation angle per second) of gimbal often results in motion blur in the captured video frames. Motion deblurring in real-time is difficult with existing blind restoration methods which incur large execution times while attempting to retrieve latent images from blurry inputs using high-dimensional optimization. On the other hand, deep learning methods for motion deblurring, though fast, do not generalize satisfactorily with domain shifts. In this work, we address the problem of real-time motion deblurring in infrared (IR) images captured by a real gimbal-based system. We propose two blur-kernel estimation methods and reveal how a priori knowledge of the blur-kernel can be used in conjunction with non-blind deblurring methods to achieve real-time performance. We experimentally show that, in comparison to the state-of-the-art techniques in deblurring, our method is better-suited for practical gimbal-based imaging systems.