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Impulse Noise Detector Performance Measure Based on Intensity Volume

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Abstract

An accurate detector performance evaluation method provides a fair comparison platform and can also support in parameter optimization for existing Impulse noise detectors in the applications of medical imaging. The Impulse noise detector performance measure (INDPM) package is widely applied as tools for quantitative comparison among detectors, which contains recall measure, accuracy measure, precision measure, specificity measure and F-measure. However, these five measures suffer from limited accuracy in correctly evaluating the performance of a detector and are not in well agreement with human subjective evaluation. To solve this problem, five new measures are proposed by introducing a new concept of intensity volume to form a new Impulse noise detector performance package (IV-INDPM). Using a standard image dataset, we conduct experimental and comparative tests with 32 different original images and 5 different existing detectors. Results demonstrate the superior performance of each new measure within IV-INDPM in reaching a much closer agreement with human subjective evaluation, compared to existing measures in INDPM. Even though five new measures are efficient in evaluating detectors’ performance from different perspectives, a new benchmark algorithm (IND-BA) is proposed as a robust and overall metric for ease of general-purpose use by making the most of these five new measures. Comparison results demonstrate its efficiency and accuracy.

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References

  1. Rodriguez, P. A., Correa, N. M., Eichele, T., Calhoun, V. D., & Adalı, T. (2011). Quality map thresholding for de-noising of complex-valued fMRI data and its application to ICA of fMRI. Journal of signal processing systems, 65(3), 497–508.

    Article  Google Scholar 

  2. Guo, L., Au, O. C., Ma, M., & Liang, Z. (2010). Fast multi-hypothesis motion compensated filter for video denoising. Journal of Signal Processing Systems, 60(3), 273–290.

    Article  Google Scholar 

  3. Tang, C., Yang, X., & Zhai, G. (2014). Robust noise estimation based on noise injection. Journal of Signal Processing Systems, 74(1), 69–78.

    Article  Google Scholar 

  4. Lan, R., Zhou, Y., Tang, Y. Y., & Chen, C. L. P. (2015) Image denoising using non-local fuzzy means. 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), Chengdu, pp. 196–200.

  5. Kamenický, J., Šroubek, F., Zitová, B., Hannuksela, J., & Turtinen, M. (2019). Image restoration in portable devices: Algorithms and optimization. Journal of Signal Processing Systems, 91(1), 9–20.

    Article  Google Scholar 

  6. Bhadouria, V. S., Tanase, A., Schmid, M., Hannig, F., Teich, J., & Ghoshal, D. (2017). A novel image impulse noise removal algorithm optimized for hardware accelerators. Journal of Signal Processing Systems, 89(2), 225–242.

    Article  Google Scholar 

  7. Bao, L., Panetta, K., & Agaian, S. (2015) A new correlation-differential denoising algorithm. In: Proc. IEEE Int. Conf. IST, Macau, China, pp. 1–6.

  8. Panetta, K., Bao, L., & Agaian, S. (2016). Sequence-to-sequence similarity-based filter for image Denoising. IEEE Sensors Journal, 16, 4380–4388.

    Article  Google Scholar 

  9. Liu, L., Chen, C. L. P., Zhou, Y., & You, X. (2015). A new weighted mean filter with a two-phase detector for removing Impulse noise. Information Sciences, 315, 1–16.

    Article  MathSciNet  Google Scholar 

  10. Liu, L., Chen, L., Chen, C. L. P., Tang, Y. Y., & Pun, C. M. (2016). Weighted Joint Sparse Representation for Removing Mixed Noise in Image. IEEE Transaction on Cybernetics, PP, 1–12.

    Google Scholar 

  11. Chen, C. L. P., Liu, L., Chen, L., Tang, Y. Y., & Zhou, Y. (2015). Weighted couple sparse representation with classified regularization for impulse noise removal. IEEE Transactions on Image Processing, 24, 4014–4026.

    Article  MathSciNet  Google Scholar 

  12. Jiang, J., Zhang, L., & Yang, J. (2014). Mixed noise removal by weighted encoding with sparse nonlocal regularization. IEEE Transactions on Image Processing, 23, 2651–2662.

    Article  MathSciNet  Google Scholar 

  13. Hosseini, H., Hessar, F., & Marvasti, F. (2015). Real-time impulse noise suppression from images using an efficient weighted-average filtering. IEEE Signal Processing Letters, 22, 1050–1054.

    Article  Google Scholar 

  14. Liu, L., Chen, C. L. P., You, X., Tang, Y. Y., Zhang, Y., & Li, S. (2017). Mixed Noise Removal via Robust Constrained Sparse Representation. IEEE Transactions on Circuits and Systems for Video Technology, PP, 1–1.

    Article  Google Scholar 

  15. Jin, K. H., & Ye, J. C. (2017). Sparse and Low-Rank Decomposition of a Hankel Structured Matrix for Impulse noise Removal. IEEE Transactions on Image Processing, PP, 1–1.

    Google Scholar 

  16. Mafi, M., Martin, H., & Adjouadi, M. (2017) High Impulse noise intensity removal in MRI images. In Proc. IEEE SPMB, Philadelphia, PA, pp. 1–6.

  17. Al-Ameen, Z., Sulong, G., Rehman, A., Al-Rodhaan, M., Saba, T., & Al-Dhelaan, A. (2017). Phase-preserving approach in denoising computed tomography medical images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 5, 16–26.

    Google Scholar 

  18. Agaian, S. S., Danahy, E. E., & Panetta, K. A. (2008). Logical System Representation of Images and Removal of Impulse noise. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 38, 1349–1362.

    Article  Google Scholar 

  19. Gellert, A., & Brad, R. (2016). Context-based prediction filtering of impulse noise images. IET Image Processing, 10, 429–437.

    Article  Google Scholar 

  20. Wang, X., Shi, G., Zhang, P., Wu, J., Li, F., Wang, Y., & Jiang, H. (2016). High quality impulse noise removal via non-uniform sampling and autoregressive modelling based super-resolution. IET Image Processing, 10, 304–313.

    Article  Google Scholar 

  21. Wang, R., Pakleppa, M., & Trucco, E. (2015). Low-rank prior in single patches for nonpointwise impulse noise removal. IEEE Transactions on Image Processing, 24, 1485–1496.

    Article  MathSciNet  Google Scholar 

  22. Chou, H. H., Hsu, L. Y., & Hu, H. T. (2013). Turbulent-PSO-based fuzzy image filter with no-reference measures for high-density impulse noise. IEEE Transactions on Cybernetics, 43, 296–307.

    Article  Google Scholar 

  23. Lien, C. Y., Huang, C. C., Chen, P. Y., & Lin, Y. F. (2013). An efficient Denoising architecture for removal of impulse noise in images. IEEE Transactions on Computers, 62, 631–643.

    Article  MathSciNet  Google Scholar 

  24. Margoosian, A., Abouei, J., & Plataniotis, K. N. (2015). An accurate Kernelized energy detection in Gaussian and non-Gaussian/impulsive noises. IEEE Transactions on Signal Processing, 63, 5621–5636.

    Article  MathSciNet  Google Scholar 

  25. Chen, Y., Zhang, Y., Shu, H., Yang, J., Luo, L., Coatrieux, J. L., & Feng, Q. (2018). Structure-adaptive fuzzy estimation for random-valued Impulse noise suppression. IEEE Transactions on Circuits and Systems for Video Technology, 28, 414–427.

    Article  Google Scholar 

  26. Ahmed, F., & Das, S. (2014). Removal of high-density salt-and-pepper noise in images with an iterative adaptive fuzzy filter using alpha-trimmed mean. IEEE Transactions on Fuzzy Systems, 22, 1352–1358.

    Article  Google Scholar 

  27. Pok, G., & Ryu, K. H. (2018). Efficient block matching for removing impulse noise. IEEE Signal Processing Letters, 25(8), 1176–1180.

    Article  Google Scholar 

  28. Zuo, C., Jovanov, L., Goossens, B., Luong, H. Q., Philips, W., Liu, Y., & Zhang, M. (2016). Image Denoising using Quadtree-based nonlocal means with locally adaptive principal component analysis. IEEE Signal Processing Letters, 23, 434–438.

    Article  Google Scholar 

  29. Huang, T., Dong, W., Xie, X., Shi, G., & Bai, X. (2017). Mixed noise removal via Laplacian scale mixture modeling and nonlocal low-rank approximation. IEEE Transactions on Image Processing, 26, 3171–3186.

    Article  MathSciNet  Google Scholar 

  30. A. Weber, “The USC-SIPI image database,” 1997.

    Google Scholar 

  31. Xiong, B., & Yin, Z. (2012). A universal denoising framework with a new impulse detector and nonlocal means. IEEE Transactions on Image Processing, 21, 1663–1675.

    Article  MathSciNet  Google Scholar 

  32. Akkoul, S., Ledee, R., Leconge, R., & Harba, R. (2010). A new adaptive switching median filter. IEEE Signal Processing Letters, 17, 587–590.

    Article  Google Scholar 

  33. Dong, Y., & Xu, S. (2007). A new directional weighted median filter for removal of random-valued impulse noise. IEEE Signal Processing Letters, 14, 193–196.

    Article  Google Scholar 

  34. Lin, C. H., Tsai, J. S., & Chiu, C. T. (2010). Switching bilateral filter with a texture/noise detector for universal noise removal. IEEE Transactions on Image Processing, 19, 2307–2320.

    Article  MathSciNet  Google Scholar 

  35. Panetta, K., Bao, L., & Agaian, S. (2018). New Image Denoising Algorithm to remove the Impulse noise. IEEE Access, 5, 37225–37236.

    Article  Google Scholar 

  36. Zhou, W., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image Qualifty assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Tufts University, Medford, MA, 02155, USA

    Long Bao & Karen Panetta

  2. Department of Computer Science, The City University of New York, Staten Island, NY, 10314, USA

    Sos Agaian

Authors
  1. Long Bao
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  2. Karen Panetta
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  3. Sos Agaian
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Correspondence to Long Bao.

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Bao, L., Panetta, K. & Agaian, S. Impulse Noise Detector Performance Measure Based on Intensity Volume. J Sign Process Syst 92, 425–434 (2020). https://doi.org/10.1007/s11265-019-01475-4

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  • DOI: https://doi.org/10.1007/s11265-019-01475-4

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