  Bias in image restoration algorithms can hamper further analysis, typically
when the intensities have a physical meaning of interest, e.g., in medical
imaging. We propose to suppress a part of the bias -- the method bias -- while
leaving unchanged the other unavoidable part -- the model bias. Our debiasing
technique can be used for any locally affine estimator including \^a1
regularization, anisotropic total-variation and some nonlocal filters.
