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 ℓ1 regularization, anisotropic total-variation and some nonlocal filters.