This paper addresses the problem of mapping images between different vision sensors. Such a mapping could be modeled as a sampling problem that has to encompass the change of geometry between the two sensors and the specific discretization of the real scene observed by the two different imaging systems. We formulate the problem in a general framework that can be cast as a minimization regularized problem with a linear operator, that applies to any image geometry. We then focus on the particular problem of the generation of planar images from omnidirectional images, in any viewing direction and for any size and resolution. In this regularized approach, the ﬁdelity term is expressed in the original omnicam geometry and the regularization is based on Total Variation (TV) solved here with proximal methods. Experimental results demonstrate the superiority of this approach with respect to alternative schemes based on linear interpolation or TV inpainting.