ITLApplied  Computational Mathematics Division
MCSD Highlights
Attractive Image NIST

Technology Transfer Center Interested in Image Deblurring Technique Developed by ITL

July 1997

The Mid-Atlantic Technology Transfer Center, College Station, Texas, has contacted NIST with a request for more details on a patent recently given to an ITL mathematician. Alfred Carasso, Mathematical and Computational Sciences Division, received a second patent for his method for digital image restoration. The center wants to publicize the invention in their widely disseminated newsletter.

Most image deblurring procedures impose smoothing assumptions on the unknown image in order to stabilize the ill-posed deblurring problem. However, in many important industrial, military, surveillance, or biomedical applications, the desired image is highly non-smooth, and reconstruction of fine detail is of prime interest. The use of smoothness constraints, required by the methods of the first patent, is particularly ill-advised in medical image deblurring, as this might result in an oversmoothed image in which vital diagnostic information regarding tumors, microcalcifications, and the like, have been eliminated.

The method is one of 56 technologies, culled from a database of 11,000 emerging technologies, that was recognized as significant for future work in an Institute of Defense Analyses report entitled "Information Warfare Technologies: Survey of Selected Civil Sector Activities" by W.J. Barlow and R.D. Turner, dated February 1996 (IDA Document D-1792).

The NIST procedure is based on a new "slow evolution" constraint, which effectively constrains the known blurring kernel, rather than the unknown solution. Since no smoothness constraints are imposed on the image, the procedure is highly effective in reconstructing fine detail. A rigorous analysis of this method appears in the SIAM Journal on Mathematical Analysis, Volume 28, Number 3, May 1997, pp. 656-668. Images deblurred via this procedure have been shown to have higher resolution than those obtained using non-linear probabilistic approaches such as the maximum entropy method, the Lucy-Richardson method, or the maximum likelihood E-M algorithm, while using far less computer time.

(bullet) Alfred S. Carasso (NIST/MCSD/MMG)

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