Non-parametric Sub-pixel Local Point Spread Function Estimation
Mauricio Delbracio, Pablo Musé, Andrés Almansa
→ BibTeX
    title   = {{Non-parametric Sub-pixel Local Point Spread Function Estimation}},
    author  = {Delbracio, Mauricio and Musé, Pablo and Almansa, Andrés},
    journal = {{Image Processing On Line}},
    volume  = {2},
    pages   = {8--21},
    year    = {2012},
    doi     = {10.5201/ipol.2012.admm-nppsf},
% if your bibliography style doesn't support doi fields:
    note    = {\url{}}
Mauricio Delbracio, Pablo Musé, and Andrés Almansa, Non-parametric Sub-pixel Local Point Spread Function Estimation, Image Processing On Line, 2 (2012), pp. 8–21.

Communicated by Sylvain Durand
Demo edited by Mauricio Delbracio


This work presents an algorithm for the local subpixel estimation of the Point Spread Function (PSF) that models the intrinsic camera blur. For this purpose, the Bernoulli(0.5) random noise calibration pattern introduced in a previous article is used. This leads to a well-posed near-optimal accurate estimation. First the pattern position and its illumination conditions are accurately estimated. This allows for accurate geometric registration and radiometric correction. Once these procedures are performed, the local PSF can be directly computed by inverting a linear system. This system is well-posed and consequently its inversion does not require any regularization or prior model. The PSF estimates reach stringent accuracy levels with a relative error in the order of 2 to 5%.


Supplementary Materials