Globally optimal reconstruction of millimeter-wave radiometric images with belief propagation

We present a new technique to reconstruct passive millimeter-wave images. To do that, we formulate the reconstruction as a global optimization through all the raw samples of the radiometer. The method assigns some labels for each pixel to be reconstructed. We then define a cost function consisting of two terms. The first one reflects the cost that each pixel assumes by taking a certain label value. The second one preserves the property of the radiometric image of being piecewise smooth. We apply belief propagation after that to minimize the error function and find the globally optimal value for each pixel. Our scheme leads to images with enhanced contrast and preserves the shape of the objects. We confirm these points via a comparison with several state of the art methods on real radiometric data.