Image fusion deals with the ability to integrate data from image sensors at different instants when the source information is uncertain. Although there exist many techniques on the subject, in this paper, we develop two originative techniques based on principal component analysis and slicing image transformation to efficiently fuse a small set of noisy images. For instance, in neural data fusion, this approach requires a considerable number of corrupted images to efficiently produce the desired outcome and also requiring a considerable computing time because of the dynamics involved in the fusion data process. In our approaches, the computation time is considerably smaller. This results appealing to increasing feasibility, for instance, in remote sensing or wireless sensor network. Moreover, and according to our numerical experiments, when our methods are compared against the neural data fusion algorithm, they present better performance.