Skip to main content
Log in

Feasibility Study for Application of Total-Variation-Based Noise-Removal Algorithm with 450-kVp High-Energy Industrial Computed-Tomography Imaging System for Non-destructive Testing

  • Published:
Experimental Techniques Aims and scope Submit manuscript

Abstract

Several important technological trends for the detection of faults in the internal structure of a material utilize industrial computed-tomography (CT) X-ray imaging systems in non-destructive testing (NDT). In this system, the total-variation-(TV)-based noise-removal algorithm is a powerful method for denoising with a high edge-information preservation. In this study, we confirm the application feasibility of the TV-based noise-removal algorithm with an established 450-kVp high-energy industrial CT imaging system for NDT. The results obtained using two phantoms (SEDENTEX cone-beam CT image-quality phantom and pressure-head phantom) with our established imaging system reveal excellent normalized noise power spectrum, contrast–to–noise ratio, and coefficient of variation of the images obtained using the TV-based noise-removal algorithm. Therefore, this study reveals that the TV-based noise-removal algorithm can improve the noise characteristics in an industrial CT imaging system for NDT.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Gonzalez J, Lambros J (2016) A parametric study on the influence of internal speckle patterning for digital volume correlation in X-ray tomography applications. Exp Tech 40:1447–1459

    Article  Google Scholar 

  2. Wang ML, Schreyer HL, Rutland GA (1991) Internal deformation measurements using real-time X-rays. Exp Tech 15:43–47

    Article  Google Scholar 

  3. Hossain S, Zheng G, Truman CE, Smith DJ (2017) Application of multiple analysis methods in optimising complex residual stress characterisation. Exp Tech 41:483–503

    Article  Google Scholar 

  4. Kang SH, Kim KY, Hwang Y, Hong JW, Baek SR, Lee CL, Lee Y (2018) A Monte Carlo simulation study for feasiblity of total variation (TV) noise reduction technique using digital mouse whole body (MOBY) phantom image. Optik 156:197–203

    Article  Google Scholar 

  5. Yu Y, Zhang D, Cheng H, Cheng L (2013) Application of wavelet denoising algorithm in nondestructive testing based on LabVIEW. Appl Mech Mater 423-426:2464–2468

    Article  Google Scholar 

  6. Kim SH, Seo K, Kang SH, Bae S, Kwak H, Hong JW, Hwang Y, Kang SM, Choi HR, Kim GY, Lee Y (2017) Study on feasiblity for artificial intelligence (AI) noise reduction algorithm with various parameters in pediatric abdominal radio-magnetic computed tomography (CT). J Magn 22:570–578

    Article  Google Scholar 

  7. Raj VNP, Venkateswarlu T (2012) Denoising of magnetic resonance and X-ray images using variance stabilization and patch based algorithms. IJMA 4:53–71

    Article  Google Scholar 

  8. Lee D, Kim YS, Choi S, Lee H, Choi S, Kim HJ (2016) A feasibility study for anatomical noise reduction in dual-energy chest digital tomosynthesis. J. Instrum 11. https://doi.org/10.1088/1748-0221/11/01/P01016

  9. Wilso M, Aidoo AY, Acquah CH (2017) Chest radiograph image enhancement: a total variation approach. IJCA 163:1–7

    Article  Google Scholar 

  10. Kwak HJ, Lee SJ, Lee Y, Lee DH (2018) Quantitative study of total variation (TV) noise reduction algorithm with chest X-ray imaging. J. Instrum. 13. https://doi.org/10.1088/1748-0221/13/01/T01006

  11. Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Physica D 60:259–268

    Article  Google Scholar 

  12. Dongmin L, Lijuan Z (2011) Study on image denoising method based on an adaptive total variation model. TMEE:2270–2273

Download references

Acknowledgments

This research was supported by the National Research Foundation of Korea (NRF-2016R1D1A1B03930357).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Y. Lee.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lee, Y. Feasibility Study for Application of Total-Variation-Based Noise-Removal Algorithm with 450-kVp High-Energy Industrial Computed-Tomography Imaging System for Non-destructive Testing. Exp Tech 43, 117–123 (2019). https://doi.org/10.1007/s40799-018-0276-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40799-018-0276-8

Keywords

Navigation