In most digital cameras, and even in high-end digital SLRs, the acquired images are sampled at rates far below the Nyquist critical rate, causing aliasing effects. This work introduces a blind algorithm for the subpixel estimation of the point spread function of a digital camera from aliased photographs. The numerical procedure simply uses two fronto parallel photographs of any planar textured scene at different distances. The mathematical theory developed herein proves that the camera psf can be derived from the inter-image kernel. Mathematical proofs supplemented by experimental evidence show the well-posedness of the problem and the convergence of the proposed algorithm to the camera in-focus psf. An experimental comparison of the resulting psf estimates shows that the proposed algorithm reaches the accuracy levels of the best nonblind state-of-the-art methods.