Due to imperfections of imaging devices (optical degradations, limited resolution of CCD sensors) and instability of the observed scene (object motion, media turbulence), acquired images are often blurred, noisy and may exhibit insufficient spatial and/or temporal resolution. Such images are not suitable for object detection and recognition. Reliable detection requires recovering the original image. If multiple images of the scene are available, this can be achieved by image fusion. In this chapter we review the respective methods of image fusion. We address all three major steps-image registration, blind deconvolution and resolution enhancement. Image registration brings the acquired images into spatial alignment, multiframe deconvolution estimates and removes the blur, and the spatial resolution of the image is increased by so-called superresolution fusion. Superresolution is the main topic of the chapter. We propose a unifying system that simultaneously estimates blurs and recovers the original undistorted image, all in high resolution, without any prior knowledge of the blurs and original image. We accomplish this by formulating the problem as constrained least squares energy minimization with appropriate regularization terms, which guarantees a close-to-perfect solution. Wedemonstrate the performance of the method on many examples, namely on car license plate recognition and face recognition. Both of these tasks are of great importance in security and surveillance systems.