Iteratively reweighted blind deconvolution

Traditional blind deconvolution techniques rely on a statistical model that relates the measured data to the pristine scene whose reconstruction is sought. If the data is not consistent with this forward model, then the reconstruction is badly degraded. We develop a way of making blind deconvolution robust to modeling errors by assigning a weight to each pixel of measured data and iteratively updating the weights. We show that this approach is effective in several realistic model-mismatch scenarios.