It is challenging to consistently smooth natural images, yet smoothing results determine the quality of a broad range of applications in computer vision. To achieve consistent smoothing, we propose a novel optimization model making use of the redundancy of natural images, by defining a nonlocal concentration regularization term on the gradient. This nonlocal constraint is carefully combined with a gradientsparsity constraint, allowing details throughout the whole image to be removed automatically in a datadriven manner. As variations in gradient between similar patches can be suppressed effectively, the new model has excellent edge preserving, detail removal,and visual consistency properties. Comparisons with state-of-the-art smoothing methods demonstrate the effectiveness of the new method. Several applications,including edge manipulation, image abstraction,detail magnification, and image resizing, show the applicability of the new method.
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