Digital image processing has become the most common form of image processing. Many transformations can be achieved by very simple and versatile algorithms such as contrast enhancing, restoration, color correction, etc. However, a wide branch of image processing algorithms make an extensive use of spatial transformations that are only defined in the analog domain such as rotation, translation, zoom, anamorphosis, homography, distortion, derivation, etc. Designing a digital image processing algorithm that mimic a spatial transformation is usually achieved by using the so-called kernel based approach. This approach involves two kernels to ensure the continuous to discrete interplay: the sampling kernel and the reconstruction kernel, whose choice is highly arbitrarily made. The maxitive kernel based approach can be seen as an extension of the conventional kernel based approach that reduces the impact of such an arbitrary choice. Replacing a conventional kernel by a maxitive kernel in a digital image spatial transformation leads to compute the convex set of all the images that would have been obtained by using a (continuous convex set) of conventional kernels. Using this set induces a kind of robustness that can reduce the risk of false interpretation. Medical imaging for example would be a kind of applications that could benefit of such an approach.