Super-resolution and image enhancement are frequently sought for in remote sensing. Many approaches were used over the years to obtain this yearned enhancement. In this article, the authors present a novel super-resolution approach based on iterative data fusion algorithms. The proposed algorithm can be implemented using either a plurality of images with varied resolution generated from different regions of the field of view, or a plurality of spectral images of the same region. The suggested approach is gradual, allowing the build-up of one single high-resolution (HR) image. The iterative procedure used in this article is based on iterative ping-pong computation between the spatial domain and its spectral distribution, similar to the approach used by Gerchberg and Saxton, and later by Gerchberg and Papoulis; however, the new approach makes use of dynamic parameters. The dynamic-iterative approach enables the retrieval of HR data from data which mostly contain low-resolution (LR) images. In both the approaches mentioned (the spatial as well as the spectral one), one may mix HR and LR information by the insertion of properly defined constraints, and achieve an enhanced image in terms of resolution, clarity, correlation with true data and contrast.