Automated registration of image frames is often required for construction of High-Resolution (HR) data to perform surveillance and threat assessment. While some efficient approaches to image registration have been developed lately, the registration algorithms resulting from these approaches generally remain application dependent and may require operator-assisted tuning for different images to achieve same efficiency levels. In this article, we describe an algorithm for automatic image registration that assists improved surveillance and threat assessment in scenarios where multiple diverse sensors are used for these applications. This algorithm offers scene-independent registration performance and is efficient for different scenes ranging from complex highly-varying gray-scale images to simpler low variable gray-scale images. While use of feature-based methods has emerged as more versatile for automatic registration in surveillance applications (compared to other methods based on correlation, mutual information maximization, etc.), the algorithm described here employs the local frequency representation for the image frames to be registered in order to generate a set of control points to solve the matching problem and to determine the registration parameters. The algorithm exploits certain inherent strong points of local frequency representation, such as robustness to illumination variation, capability of detecting the structure of the scene in the image (ridges and edges) simultaneously, and good localization in spatial domain. Experimental results reported here indicate that this registration technique is efficient and yields promising results for the alignment and fusion of complex images.