-Inspired by the visual system of many mammals, we consider the construction of-and reconstruction from-an orientation score of an image, via a wavelet transform corresponding to the left-regular representation of the Euclidean motion group in ތ 2 ( ޒ 2 ) and oriented wavelet ψ ∈ ތ 2 ( ޒ 2 ). Because this representation is reducible, the general wavelet reconstruction theorem does not apply. By means of reproducing kernel theory, we formulate a new and more general wavelet theory, which is applied to our specific case. As a result we can quantify the well-posedness of the reconstruction given the wavelet ψ and deal with the question of which oriented wavelet ψ is practically desirable in the sense that it both allows a stable reconstruction and a proper detection of local elongated structures. This enables image enhancement by means of left-invariant operators on orientation scores.