Blind subpixel Point Spread Function estimation from scaled image pairs
Résumé
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 non-blind state-of-the-art methods.
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