Blind subpixel Point Spread Function estimation from scaled image pairs

Mauricio Delbracio (1, 2) , Andrés Almansa (3) , Jean-Michel Morel (1) , Pablo Musé (2)

1 CMLA - Centre de Mathématiques et de Leurs Applications
2 IIE - Instituto de Ingeniería Eléctrica
3 TSI - Département Traitement du Signal et des Images

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.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Mauricio Delbracio, Andrés Almansa, Jean-Michel Morel, Pablo Musé. Blind subpixel Point Spread Function estimation from scaled image pairs. 2011. ⟨hal-00624757v1⟩