Abstract – Optical aberrations of a lens introduce lens blur to photographed images. Lens blur is non-stationary with the amount and characteristics of blur varying depending on spatial pixel locations in an image. This work presents non-stationary deep networks for the restoration of non-stationary lens blur. Deep networks have relatively larger receptive fields. However, the receptive fields of stationary deep networks are not wide enough for the networks to cope with the non-stationarity of lens blur that span the entire image. We use spatial pixel locations as an additional input to networks to let the network utilize location dependent features to handle the non-stationarity. Experimental results show that even shallower non-stationary networks provide better performance than deeper stationary networks. The non-stationary networks are trained from pairs of images photographed at different aperture settings, eliminating the necessity of estimation or measurement of pixel-wise variant non-stationary lens blur.