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Advanced Processing Methodologies Improve Neutron Radiograph Image Quality

We propose a novel processing protocol framework for neutron radiograph image quality improvement. Neutron radiographs were collected at the Oak Ridge National Laboratory (ORNL) High Flux Isotope Reactor (HFIR) CG-1B imaging station utilizing a 2.35 Å cold neutron beam with flux ~107 n/cm2/sec. A 100 micron thick ZnS(Ag)/6LiF scintillator was coupled via mirror to a second generation sCMOS camera system with 2k x 2k spatial resolution. We address image quality degradations due to random spuriously detected gamma rays (spikes), camera dark current and readout noise (background), detection system resolution fall-off with increasing spatial frequency, and Gaussian noise due to detection counting statistics. The background is estimated and subtracted out. Gaussian noise is estimated and removed via 3D transform-domain collaborative filtering to minimize effects of spatial resolution degradation. Spikes are removed via an adaptive morphological filter. Capitalizing upon the resultant low noise images produced, Metz restoration filtering is employed to significantly improve image sharpness with increasing spatial resolution. The order of operations for filtering is important, and we restore very low noise images with ~70 micron resolution using our processing protocol.

1. Li, Hongyun, B. Schillinger, E. Calzada, L. Yinong and M. Muehlbauer, “An Adaptive Algorithm for Gamma Spots Removal in CCD-based Neutron Radiography and Tomography,” Nucl. Instrum. Methods Phys. Res. Sect. A-Accel. Spectrom. Dect. Assoc. Equip., Vol 564, Issue 1, pp. 405-413, Aug. 2006. 2. Wei, Jin, “Image restoration in neutron radiography using complex-wavelet denoising and lucy-richardson deconvolution,” 8th International Conference on Signal Processing, Vol. 2, no., 16-20 2006. 3. Osterloh, K., T. Bücherl, Ch. Lierse von Gostomski, U. Zscherpel, U. Ewert and S. Bock, “Filtering algorithm for dotted interferences,” Nucl. Instrum. Methods Phys. Res. Sect. A-Accel. Spectrom. Dect. Assoc. Equip., Vol 651, Issue 1, pp 171-174, Sep 2011. 4. Aharon, M., M. Elad and A. Bruckstein, “K -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation,” IEEE Trans. on Signal Processing, Vol.54, no.11, pp. 4311-4322, Nov. 2006. 5. Dabov, K., A. Foi., V. Katkovnik and K. Egiazarian, “Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering,” IEEE Trans. on Image Processing, Vol.16, no.8, pp. 2080-2095, Aug. 2007. 6. Zhou, M., H. Chen, J. Paisley, R. Lu, L. Li, Z. Xing, D. Dunson, G. Sapiro and L. Carin, “Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images,” IEEE Trans. on Image Processing, Vol.21, no.1, pp. 130-144, Jan. 2012. 7. 8. Jasti, J. K. and H.S. Fogler, “Application of neutron radiography to image flow phenomena in porous media,” AIChE Journal 38(4): 481-488. 1992. 9. Gonzalez, R.C. and Richard E. Woods, Digital Image Processing, Prentice Hall, 3rd edition, Aug. 2007. 10. Ma, King, B.C. Penney and S.J. Glick, “An image-dependent Metz filter for nuclear medicine images,” J Nucl Med., 29 (12):1980-9, Dec. 1988.
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