Automatic acquisition of moving objects from long-distance video sequence is a fundamental task in many applications such as surveillance and reconnaissance. However, the atmospheric degradations, which include blur and spatiotemporal-varying distortions, may reduce the quality of such videos, and therefore, the ability to acquire moving targets automatically. Pervious studies in the field of automatic acquisition of moving objects ignored the blur in the video frames. They usually employed simple methods for noise reduction (such as temporal and spatial smoothing) and motion compensation (registration of frames). The purpose of this work is to determine the effect of image restoration (de-blurring) on the ability to acquire moving objects (such as humans and vehicles) automatically. This is done here by first, restoring the long-distance thermal videos using a novel blind image deconvolution method developed recently, and then comparing the automatic acquisition capabilities in the restored videos versus the non-restored versions. Results show that image restoration can significantly improve the automatic acquisition capability. These results correspond to a previous study which demonstrated that image restoration can significantly improve the ability of human observers to acquire moving objects from a long-range thermal video.