In this paper, we present a review of the research literature regarding applying X-ray imaging of baggage scrutiny at airport. It discusses multiple X-ray imaging inspection systems used in airports for detecting dangerous objects inside the baggage. Moreover, it also explains the dual energy X-ray image fusion and image enhancement factors. Different types of noises in digital images and noise models are explained in length. Diagrammatical representations for different noise models are presented and illustrated to clearly show the effect of Poisson and Impulse noise on intensity values. Overall, this review discusses in detail of Poisson and Impulse noise, as well as its causes and effect on the X-ray images, which create un-certainty for the X-ray inspection imaging system while discriminating objects and for the screeners as well. The review then focuses on image processing techniques used by different research studies for X-ray image enhancement, de-noising, and their limitations. Furthermore, the most related approaches for noise reduction and its drawbacks are presented. The methods that may be useful to overcome the drawbacks are also discussed in subsequent sections of this paper. In summary, this review paper highlights the key theories and technical methods used for X-ray image enhancement and de-noising effect on X-ray images generated by the airport baggage inspection system.
MaylesP.et al., “Handbook of radiotherapy physics: Theory and Practice”. CRC Press (1st Ed.), 442–443, 2007.
4.
SchaferD.et al., New X-ray technology for the detection of explosives, First International Symposium on Explosive Detection Technology, Atlantic City, NJ, pp. 269–281, 1991.
5.
EilbertR.F. and KrugK.D., “Aspects of image recognition in vivid technology’s dual-energy x-ray systemfor explosive detection,” International Society for Optics and Photonics (SPIE), USA 127–143 (1992), 1824.
SameerS. and ManeeshaS., Explosives detection systems (EDS) for aviation security, Journal Signal Processing83(1) (2003), 31–55.
15.
ChenZ.et al., “A combinational approach to the fusion, de-noising, and enhancement of dual-energy x-ray luggage images, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops2(2), 2005.
16.
KhanS.U.et al., “A novel noise removal technique of X-Ray carry-on luggage for detection of contraband/illicitObject(s),”), International Journal of Engineering and Advance Technology (IJEAT2(3), 2013.
HanP.et al., “An efficient enhancement technique of X-ray carry-on luggage images,” 9th International Conference on Signal Processing, Beijing, pp. 1170–1173, 2008.
21.
HeX.P.et al., “A new enhancement technique of x-ray carry-on luggage images based on DWT and fuzzy theory,” International Conference on Computer Science and Information Technology, ICCSIT ’08, Singapore, pp. 855–858, 2008.
ToranL.S. and DeeptyD., A survey on image noises and de-noises techniques, International Journal of Advanced Research in Computer Engineering & Technology1(9), 2012.
24.
PawanP.et al., Image de-noising by various filters for different noise, International Journal of Computer Applications9(4) (2010), 0975–8887.
25.
AfroseZ., Relaxed median filter: A better noise removal filter for compound images, International Journal on Computer Science and Engineering (IJCSE)4(7) (2012), 13–36.
26.
BovikA.C., Image noise models, Handbook of Image and Video Processing (2nd Ed.), 1355–1372, 2005.
27.
SakataM. and OgawaK., “Noise reduction and contrast enhancement for small-dose X-ray images in wavelet domain,” IEEE Nuclear Science Symposium Conference Record (NSS/MIC), pp. 2924–2929, 2009.
28.
KwonJ.W.et al., “Noise reduction in DEXA image based on system noise modeling,” International Conference on Biomedical and Pharmaceutical Engineering, (ICBPE), Singapore, pp. 1–6, 2009.
29.
AwaisM.F. and RohankarS.J., Survey on various noises and techniques for de-noising the color image, International Journal of Application or Innovation in Engineering & Management (IJAIEM)2(11), 2013.
30.
SedefK.et al., “Speckle reduction of synthetic aperture radar images usingwavelet filtering,” 5th European Conference on Synthetic Aperture Radar, EUSAR’2004, Ulm, Germany, pp. 25–27, 2004.
31.
RaneyR.K., “Radar Fundamentals: Technical Perspective,” Chapter 2 in Principles and Applications of Imaging Radar, Manual of Remote Sensing, (3rd Ed.), 2, ASPRS, John Wiley and Sons Inc., Toronto, 1998.
32.
SandhyaK.T.et al., “A fast and improved switching median filter with adaptive window for impulse noise removal,”, International Journal of Engineering Research & Technology1(10), 2012.
33.
NasimudeenA.et al., Directional switching median filter using boundary discriminative noise detection by elimination, Journal of Signal, Image and Video Processing6(4) (2012), 613–624.
34.
NairM.S. and ShankarV., Predictive-based adaptive switching median filter for impulse noise removal using neural network-based noise detector, Signal, Image and Video Processing7(6) (2012), 1041–1070.
35.
WandL.et al., “Noise reduction using wavelet with application to medical X-ray image,” IEEE International Conference on Industrial Technology, (ICIT 2005), Hong Kong, pp. 33–38. 2005.
36.
KennethF.et al., The application of wavelet de-noising in material discrimination system, Proc SPIE7538 (2010), 75380Z.
37.
VijaykumarV.R.et al., Fast and efficient algorithm to remove gaussian noise in digital images, International Journal of Computer Science37(1), 2010.
38.
KhanS.U. and UllahS., A survey on X-ray image de-noising by various filters for different noise, International Journal of Research2(5) (2014), 277–281.
39.
NercessianS.et al., “Automatic detection of potential threat objects in X-ray luggage scan images,” IEEE Conference on Technologies for Homeland Security, Waltham, MA, pp. 504–509, 2008.
40.
AbidiB.R.et al., Improving the detection of low-density weapons in x-ray luggage scans using image enhancement and novel scene-decluttering techniques, Journal of Electronic Imaging13(3) (2004), 523–538.
41.
HuangT.S.et al., Fast two-dimensional median filtering algorithm, IEEE Transaction on Acoustics, Speech, Signal Processing27(1) (1979), 13–18.
42.
YangR.et al., Optimal weighted median filters under structural constraints, IEEE Transaction on Signal Processing43(3) (1995), 591–604.
43.
SunT. and NeuvoY., Detail-preserving median based filters in image processing, Pattern Recognition. Letters15(4) (1994), 341–347.
44.
PeiL.et al., Application of wavelet-based image fusion in image enhancement, 3rd International Congress on Image and Signal Processing (CISP)2 (2010), 649–653.
45.
FranzelT.et al., “Object detection in multi-view X-ray images,”SpringerBerlin Heidelberg, pp. 144–154, 2012.
46.
MazoochiA.et al., A novel numerical method to eliminate thickness effect in dual energy X-ray imaging used in baggage inspection, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment763 (2014), 538–542.
47.
MartinL.K.et al., “Structure-preserving dual-energy CT for luggage screening,” In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1175–1179, 2014.
48.
WoodellG.et al., “Enhanced images for checked and carry-on baggage and cargo screening,” Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense III, pp. 582, 2004.
49.
HosseinP.et al., A novel weapon detection algorithm in X-ray dual-energy images based on connected component, Australian Journal of Basic and Applied Sciences5(12) (2011).
50.
HarikiranB.et al., Impulse noise removal in digital images, International Journal of Computer Applications10(8) (2010), 0975–8887.
51.
ZhouY.et al., “3D CT baggage image enhancement based on order statistic decomposition,” International Conference on Technologies for Homeland Security (HST), Waltham, MA, 2010. doi: 10.1109/THS.2010.5655042.
52.
XiaJ.et al., An efficient two-state switching median filter for the reduction of impulse noises with different distributions, 3rd International Congress on Image and Signal Processing (CISP)2 (2010), 639–644.
53.
AbreuE.et al., A new efficient approach for the removal of impulse noise from highly corrupted images, IEEE Transaction on Image Processing5(6) (1996), 1012–1025.
54.
WangZ. and ZhangD., Progressive switching median filter for the removal of impulse noise from highly corrupted images, IEEE Transaction on Circuits and Systems-II: Analog and Digital Signal Processing46(1) (1999), 78–80.
55.
NallaperumalK.et al., “An efficient switchingmedian filter for salt & pepper impulse noise reduction,” 1st International Conference on Digital Information Management, Bangalore, pp. 161–166, 2007.
56.
NallaperumalK.et al., “Fuzzy optimal threshold multiscale morphological segmentation of digital image,” International Conference on Wireless and Optical Communication Network, Bangalore, pp. 5, 2006.
57.
EngH.L.et al., Noise adaptive soft-switching median filter, IEEE Transaction on Image Processing10(2) (2001), 242–251.
58.
NgP.E.et al., A switching median filter with boundary discriminative noise detection for extremely corrupted image, IEEE Transaction on Image Processing15(6) (2006), 1506–1516.
59.
AshimaM. and TayalA., Impulse noise detection and filtering in switching median filters, International Journal of Computer Applications45(13) (2012), 40–45.
60.
KhanS.U.et al., An algorithm for bone crack isolation and enhancement in X-ray images, International Journal of Computer Science and Information Security14(8) (2016), 621–638.
61.
KhanS.U.et al., X-ray image enhancement using boundary division wiener filters and wavelet-based image fusion approach, Journal of Information Processing Systems12(1) (2016), 35–45.
62.
IonelaN.A. and MonicaB., Satellite image improvement using phase information and wavelet transform, 8th International Conference on Communications (COMM), Bucharest, pp. 137–140, 2010.
63.
GozaniT., The role of neutron based inspection techniques in the post 9/11/ 01 era, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms213 (2004), 460–463.
64.
AbidiB.R.et al., “Grayscale enhancement techniques of x-ray images of carry-on luggage,” Proceeding of the 6th International Conference on Quality Control by Artificial Vision, 5132, Gatlinburg, TN, pp. 79–591, 2003.
65.
ChurchJ.et al., “A spatial median filter for noise removal in digital images,” In Southeastcon. IEEE, 618–623, 2008.
66.
CastellanoG.et al., An FPGA oriented Algorithm for real-time filtering of poisson noise in video streams, with application to X-ray fluoroscopy, Circuits Syst Signal Process38 (2019), 32–69.
KhanS.U.et al., Layer discrimination with non-zero adjacent element elimination and pixel restoration wiener filter approach for impulse & Poisson noise removal from X-ray images, Science International26(3) (2014), 1045–1049.
69.
MoutonA. and BreckonT.P., “A review of automated image understanding with 3D baggage computed tomography security screening, Journal of X-ray Science and Technology23(5) (2015), 531–555.
70.
WangQ.et al., An approach for adaptive automatic threat recognition within 3D computed tomography images for baggage security screening, Journal of X-ray Science and Technology28(1) (2020), 35–58.
