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Deconvolving LADAR Images Of Bar Codes for Construction Site Object Recognition



David E. Gilsinn, Geraldine S. Cheok, Dianne M. O'Leary


This report discusses a general approach to reconstructing ground truth intensity images of bar codes that have been distorted by LADAR optics. The first part of this report describes the experimental data collection of several bar code images along with experimentally obtained estimates of the LADAR beam size and configuration at various distances from the source. Mathematical models of the beam size and configuration were developed and were applied through a convolution process to a simulated set of bar code images similar to the experiment. This was done in order to estimate beam spread models (beam spread models are unique to each specific LADAR) to be used in a deconvolution process to reconstruct the original bar code images from the distorted images. In the convolution process a distorted image in vector form g is associated with a ground truth image f and each element of g is computed as a weighted average of elements of f that are neighbors to that associated element. The deconcolution process form Hf = g where H is a large sparse matrix that is made up of elements from the beam spread function. The results of applying the several beam spread models to deconvolving the bar code images are given. Deconvolution of data measured at 10 m was more successful than that for 20 m or 40 m. The appendices include more detailed discussion of the least squares algorithm used and sample programs used during the various phases of the analysis.
NIST Interagency/Internal Report (NISTIR) - 7044
Report Number


bar codes, beam spread, deconvolution, image processing, LADAR, object recognition sparse matrix


Gilsinn, D. , Cheok, G. and O'Leary, D. (2003), Deconvolving LADAR Images Of Bar Codes for Construction Site Object Recognition, NIST Interagency/Internal Report (NISTIR), National Institute of Standards and Technology, Gaithersburg, MD (Accessed April 23, 2024)
Created October 1, 2003, Updated February 19, 2017