Deconvolution techniques are widely used for image enhancement from microscopy to astronomy. The most effective methods are based on some iteration techniques, including Bayesian blind methods or Greedy algorithms. The stopping condition is a main issue for all the non-regularized methods, since practically the original image is not known, and the estimation of quality is based on some distance between the measured image and its estimated counter-part. This distance is usually the mean square error (MSE), driving to an optimization on the Least-Squares measure. Based on the independence of signal and noise, we have established a new type of error measure, checking the orthogonality criterion of the measurement driven gradient and the estimation at a given iteration. We give an automatic procedure for estimating the stopping condition. We show here its superiority against conventional ad-hoc non-regularized methods at a wide range of noise models.