We present a novel Bayesian method for pattern recognition in images affected by unknown optical degradations and additive noise. The method is based on a multiscale/multiorientation subband decomposition of both the matched filter (original object) and the degraded images. Using this image representation within the Bayesian framework, it is possible to make a coarse estimation of the unknown optical transfer function, which strongly simplifies the Bayesian estimation of the original pattern that most probably generated the observed image. The method has been implemented and compared to other previous methods through a realistic simulation. The images are degraded by different levels of both random (atmospheric turbulence) and deterministic (defocus) optical aberrations, as well as additive white Gaussian noise. The Bayesian method proved to be highly robust to both optical blur and noise, providing rates of correct responses significantly better than previous methods.