Characteristic patterns of an inhomogeneous imaging system with an application to vision.

We examine consequences of image-forming inhomogeneity in the form of a point-spread function that changes with position on the image plane. The familiar self-replicating sinusoids, which a homogeneous system simply multiplies by its spatial modulation-transfer function, generalize to eigenfunctions, which the system multiplies by eigenvalues. We give a way to calculate the eigenfunctions and eigenvalues from the variable point-spread function. We illustrate this with data from the visual system and show that these lead to a discrete set of most-sensitive eigenfunctions, which we construct.