Résumé
Summary form only given. Digital image processing refers to the set of algorithms used to transform, filter, enhance, modify, analyze, distort, fuse, etc., digital images. Most of these algorithms are designed to mimic an underlying physical operation defined in the continuous illumination domain and formerly achieved via optical or electronic filters or through manipulations, including painting, cutting, moving or pasting of image patches. It also allows more sophisticated transformations (associated to more or less complex algorithms) which would be impossible to process by analog means. It may be quite hard to completely transpose an operation from the continuous to the discrete domain. Such a transposition usually relies on methods that ensure a kind of interplay between continuous and discrete domains. The interplay between the continuous and the discrete domain usually involves a convolution with a point spread function, when the measurement model is supposed to be linear, while the interplay between the discrete and the continuous domain is ensured by interpolation or more generally approximation methods, which also involve a convolution with a reconstruction kernel. Performing a precise identification of the point spread function of an imager is usually pretty challenging. Moreover, modeling the imaging process by a point spread function could be considered as an approximation of a more complex (and not shift-invariant) phenomenon (e.g. radial distortion or chromatic aberrations).