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Bias in image restoration algorithms can hamper further analysis, typicallywhen the intensities have a physical meaning of interest, e.g., in medicalimaging. We propose to suppress a part of the bias -- the method bias -- whileleaving unchanged the other unavoidable part -- the model bias. Our debiasingtechnique can be used for any locally affine estimator including \^a1regularization, anisotropic total-variation and some nonlocal filters.

On debiasing restoration algorithms: applications to total-variation and nonlocal-means