Adaptive Reconstruction of Millimeter-Wave Radiometric Images

We present a robust method to reconstruct a millimeter-wave image from a passive sensor. The method operates directly on the raw samples from the radiometer. It allocates for each pixel to be estimated a patch in the space formed by all the raw samples of the image. It then estimates the noise in the patch by measuring some distances that reflect how far the samples are from forming a piecewise smooth surface. It then allocates a weight for each sample that defines its contribution in the pixel reconstruction. This is done via a smoothing Kernel that enforces the distances to have a piecewise smooth variation inside the patch. Results on real datasets show that our scheme leads to more contrast and less noise and the shape of an object is better preserved in a constructed image compared to state-of-the-art schemes. The proposed scheme produces better results even with low integration times, i.e., 10% of the total integration time used in our experiments.