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Enhanced statistical nearest neighbors with steerable pyramid transform for Gaussian noise removal in a color image

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Abstract

Image denoising is the foremost issue in the field of image processing and computer vision applications and the most challenging part in image denoising is to protect the data bearing structures like surfaces and edges to achieve better visual image quality. The non-local means filtering technique perform well to denoise Gaussian noise, while preserving the edges and details of the original images, In this paper, an effective Gaussian denoiser is proposed based on non-local means filter to improve the resulting image quality. An exponential kernel function is included in the statistical nearest neighbor to decrease the prediction error in noise free patches, which deblurs the lower contrast image details effectively. Further, steerable pyramid transform is applied along with hard thresholding to generate the image with better visible level. The simulation outcome showed that the proposed enhanced statistical nearest neighbors with steerable pyramid transform model achieved better Gaussian denoising performance in terms of feature similarity index, structural similarity index, mean structural similarity index, peak signal-to-noise ratio, and feature similarity index with chromatic information. In the experimental section, proposed model showed a maximum of 2.07 dB and minimum of 0.70 dB improvement in peak signal-to-noise ratio value compared to the existing model; non-local means- statistical nearest neighbor and attention-guided denoising convolutional neural network.

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References

  1. Tian C, Xu Y, Li Z, Zuo W, Fei L, Liu H (2020) Attention-guided CNN for image denoising. Neural Netw 124:117–129. https://doi.org/10.1016/j.neunet.2019.12.024

    Article  Google Scholar 

  2. Tian C, Xu Y, Zuo W (2020) Image denoising using deep CNN with batch renormalization. Neural Netw 121:461–473. https://doi.org/10.1016/j.neunet.2019.08.022

    Article  Google Scholar 

  3. Thanh DNH, Hien NN, Prasath S (2020) Adaptive total variation L1 regularization for salt and pepper image denoising. Optik 208:163677. https://doi.org/10.1016/j.ijleo.2019.163677

    Article  Google Scholar 

  4. Valsesia D, Fracastoro G, Magli E (2020) Deep graph-convolutional image denoising. IEEE Trans Image Process 29:8226–8237. https://doi.org/10.1109/TIP.2020.3013166

    Article  MathSciNet  Google Scholar 

  5. Yang X, Xu Y, Quan Y, Ji H (2020) Image denoising via sequential ensemble learning. IEEE Trans Image Process 29:5038–5049. https://doi.org/10.1109/TIP.2020.2978645

    Article  Google Scholar 

  6. Hou Y, Xu J, Liu M, Liu G, Liu L, Zhu F, Shao L (2020) NLH: a blind pixel-level non-local method for real-world image denoising. IEEE Trans Image Process 29:5121–5135. https://doi.org/10.1109/TIP.2020.2980116

    Article  Google Scholar 

  7. Shukla AK, Pandey RK, Yadav S, Pachori RB (2020) Generalized fractional filter-based algorithm for image denoising. Circuits Syst Signal Process 39:363–390. https://doi.org/10.1007/s00034-019-01186-y

    Article  Google Scholar 

  8. Fang F, Li J, Yuan Y, Zeng T, Zhang G (2020) Multilevel edge features guided network for image denoising. Neural Netw Learn Syst IEEE Trans. https://doi.org/10.1109/TNNLS.2020.3016321

    Article  Google Scholar 

  9. Li D, Chen H, Jin G, Jin Y, Zhu C, Chen E (2020) A multiscale dilated residual network for image denoising. Multimedia Tools Appl. https://doi.org/10.1007/s11042-020-09113-z

    Article  Google Scholar 

  10. Pang ZF, Zhang HL, Luo S, Zeng T (2020) Image denoising based on the adaptive weighted TVp regularization. Signal Process 167:107325. https://doi.org/10.1016/j.sigpro.2019.107325

    Article  Google Scholar 

  11. Zeng H, Xi X, Kong W, Cui S, Ning J (2020) Hyperspectral image denoising via combined non-local self-similarity and local low-rank regularization. IEEE Access 8:50190–50208. https://doi.org/10.1109/ACCESS.2020.2979809

    Article  Google Scholar 

  12. Shukla AK, Pandey RK, Reddy PK (2020) Generalized fractional derivative based adaptive algorithm for image denoising. Multimedia Tools Appl. https://doi.org/10.1007/s11042-020-08641-y

    Article  Google Scholar 

  13. Shin YH, Park MJ, Lee OY, Kim JO (2020) Deep orthogonal transform feature for image denoising. IEEE Access 8:66898–66909. https://doi.org/10.1109/ACCESS.2020.2986827

    Article  Google Scholar 

  14. Elhoseny M, Shankar K (2019) Optimal bilateral filter and convolutional neural network based denoising method of medical image measurements. Measurement 143:125–135. https://doi.org/10.1109/ACCESS.2020.2986827

    Article  Google Scholar 

  15. Kumar A, Ahmad MO, Swamy MNS (2019) Image denoising via overlapping group sparsity using orthogonal moments as similarity measure. ISA Trans 85:293–304. https://doi.org/10.1016/j.isatra.2018.10.030

    Article  Google Scholar 

  16. Zha Z, Zhang X, Wang Q, Bai Y, Chen Y, Tang L, Liu X (2018) Group sparsity residual constraint for image denoising with external nonlocal self-similarity prior. Neurocomputing 275:2294–2306. https://doi.org/10.1016/j.neucom.2017.11.004

    Article  Google Scholar 

  17. Fan L, Li X, Fan H, Zhang C (2019) An adaptive boosting procedure for low-rank based image denoising. Signal Process 164:110–124. https://doi.org/10.1016/j.sigpro.2019.06.004

    Article  Google Scholar 

  18. Wang G, Liu Y, Xiong W, Li Y (2018) An improved non-local means filter for color image denoising. Optik 173:157–173. https://doi.org/10.1016/j.ijleo.2018.08.013

    Article  Google Scholar 

  19. Li H, Suen CY (2016) A novel non-local means image denoising method based on grey theory. Pattern Recognit 49:237–248. https://doi.org/10.1016/j.patcog.2015.05.028

    Article  Google Scholar 

  20. Chen G, Zhang P, Wu Y, Shen D, Yap PT (2016) Denoising magnetic resonance images using collaborative non-local means. Neurocomputing 177:215–227. https://doi.org/10.1016/j.neucom.2015.11.031

    Article  Google Scholar 

  21. Wang X, Shen S, Shi G, Xu Y, Zhang P (2016) Iterative non-local means filter for salt and pepper noise removal. J Visual Commun Image Represent 38:440–450. https://doi.org/10.1016/j.jvcir.2016.03.024

    Article  Google Scholar 

  22. Frosio I, Kautz J (2018) Statistical nearest neighbors for image denoising. IEEE Trans Image Process 28:723–738. https://doi.org/10.1109/TIP.2018.2869685

    Article  MathSciNet  MATH  Google Scholar 

  23. Rakhshanfar M, Amer MA (2019) Efficient cascading of multi-domain image Gaussian noise filters. J Real-Time Image Proc. https://doi.org/10.1007/s11554-019-00868-9

    Article  Google Scholar 

  24. Hounsou N, Sanda Mahama AT, Gouton P, Thomas JB (2018) Comparative study of biorthogonal wavelets accuracy in demosaicing algorithm based on wavelet analysis of luminance component. Electronic Imaging 2018:362–371. https://doi.org/10.2352/ISSN.2470-1173.2018.16.COLOR-362

    Article  Google Scholar 

  25. Wu H, Jia L, Meng Y, Liu X, Lan J (2018) A novel adaptive non-local means-based nonlinear fitting for visibility improving. Symmetry 10:741. https://doi.org/10.3390/sym10120741

    Article  Google Scholar 

  26. Alelaiwi A, Abdul W, Dewan MS, Migdadi M, Muhammad G (2016) Steerable pyramid transform and local binary pattern based robust face recognition for e-health secured login. Comput Electr Eng 53:435–443. https://doi.org/10.1016/j.compeleceng.2016.01.008

    Article  Google Scholar 

  27. Li L, Yu X, Jin Z, Zhao Z, Zhuang X, Liu Z (2020) FDnCNN-based image denoising for multi-labfel localization measurement. Measurement 152:107367

    Article  Google Scholar 

  28. Zhang F, Fan H, Liu P, Li J (2020) Image denoising using hybrid singular value thresholding operators. IEEE Access 8:8157–8165. https://doi.org/10.1109/ACCESS.2020.2964683

    Article  Google Scholar 

  29. Hore A, Ziou D (2010) Image quality metrics: PSNR vs. SSIM. In 2010 20th international conference on pattern recognition, IEEE, 2366-2369. https://doi.org/10.1109/ICPR.2010.579

  30. Zhang L, Zhang L, Mou X, Zhang D (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20:2378–2386. https://doi.org/10.1109/tip.2011.2109730

    Article  MathSciNet  MATH  Google Scholar 

  31. Awad A (2019) Denoising images corrupted with impulse, Gaussian, or a mixture of impulse and Gaussian noise. Eng Sci Technol Int J 22:746–753. https://doi.org/10.1016/j.jestch.2019.01.012

    Article  Google Scholar 

  32. Huang XL, Ma X, Hu F (2018) Machine learning and intelligent communications. Mobile Netw Appl 23(1):68–70

    Article  Google Scholar 

  33. Huang XL, Tang X, Huan X, Wang P, Wu J (2018) Improved KMV-cast with BM3D denoising. Mobile Netw Appl 23(1):100–107

    Article  Google Scholar 

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Correspondence to Akula Suneetha.

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Suneetha, A., Reddy, E.S. Enhanced statistical nearest neighbors with steerable pyramid transform for Gaussian noise removal in a color image. Evol. Intel. 15, 2139–2151 (2022). https://doi.org/10.1007/s12065-021-00627-5

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  • DOI: https://doi.org/10.1007/s12065-021-00627-5

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