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A curvelet-based multi-sensor image denoising for KLT-based image fusion

  • 1193: Intelligent Processing of Multimedia Signals
  • Published:
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Abstract

The transform-based multi-sensor image denoising methods are inefficient in restoring fine details and texture information of noisy images. The fixed and non-adaptive curvelet transform (CT) design limits its performance in image denoising tasks. Moreover, the Karhunen-Loeve Transform (KLT)-based multi-sensor image fusion techniques premise that high variance’s first two principal components are an excellent option for weights used for the weighted average multi-sensor source images. However, the selected weights are non-optimal in this method, considering the less relevant information of source images. The experimental section has several examples showing the key advantages of the proposed optimized CT-based natural image denoising technique over seven existing denoising methods. First, our image denoising method introduces a modified Meyer window (used in unequally-spaced fast Fourier transform (USFFT))-based novel optimized USFFT CT (OUSFFT CT) and a modified wrapping window (WW)-based novel optimized WW CT (OWW CT) to address non-adaptive nature of curvelet transform. These windows are used for the decomposition of noisy source images into low- and high-frequency coefficients. The coefficients are hard thresholds to remove the noisy artifacts in the source image. Moreover, the denoised images are used for fusion purposes to obtain fused images with less noise. Secondly, our proposed image fusion method presents an optimized algorithm to fuse multi-sensor source images. In this method, KLT based weights are optimized by considering more relative information of source images and improve the fused image’s information interpretation capability. The qualitative and quantitative evaluations of fused images show that our method provides better fusion results than five different state-of-the-art medical, multi-focus, and infrared image fusion methods. The proposed image denoising method has 1% and 2.2% increment on average of PSNR and SSIM values, respectively, compared to existing state-of-the-art methods. The proposed image fusion method has a 9.04% increment on the average value of image fusion metrics compared to existing state-of-the-art methods.

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References

  1. https://homepages.cae.wisc.edu/~ece533/images/ (access on 26/01/2019)

  2. http://www.imageprocessingplace.com/DIP-3E/dip3e_book_images_downloads.htm(access on 11/03/2021)

  3. Achanta SDM, Karthikeyan T, Vinothkanna R (2019) A novel hidden markov model-based adaptive dynamic time warping (hmdtw) gait analysis for identifying physically challenged persons. Soft Computing 23(18):8359–8366

    Article  Google Scholar 

  4. Achanta SDMTK, RVK (2020) A wireless iot system towards gait detection technique using fsr sensor and wearable iot devices. Int J Intell Unmanned Syst 8(1):43–54

  5. Aishwarya N, Bennila Thangammal C (2018) An image fusion framework using morphology and sparse representation. Multimed Tools Appl 77(8):9719–9736

  6. Aymaz S, Köse C, Aymaz Ş (2020) Multi-focus image fusion for different datasets with super-resolution using gradient-based new fusion rule. Multimed Tools Appl 79(19):13311–13350

    Article  Google Scholar 

  7. Bavirisetti DP, Dhuli R (2016) Fusion of infrared and visible sensor images based on anisotropic diffusion and karhunen-loeve transform. IEEE Sens J 16(1):203–209

    Article  Google Scholar 

  8. Bhateja V, Patel H, Krishn A, Sahu A, Lay-Ekuakille A (2015) Multimodal medical image sensor fusion framework using cascade of wavelet and contourlet transform domains. IEEE Sens J 15(12):6783–6790

    Article  Google Scholar 

  9. Bhatnagar G, Wu QJ, Liu Z (2013) Directive contrast based multimodal medical image fusion in nsct domain. IEEE Transact Multimed 15(5):1014–1024

    Article  Google Scholar 

  10. Candes E, Demanet L, Donoho D, Ying L (2006) Fast discrete curvelet transforms. Multiscale Model Simul 5(3):861–899

    Article  MathSciNet  MATH  Google Scholar 

  11. Candes EJ,  Donoho D (2000) Curvelets - a surprisingly effective nonadaptive representation for objects with edges. Curves Surf

  12. Chavez Jr PC, Sides S, Anderson AJ (1991) Comparison of three different methods to merge multiresolution and multispectral data: Landsat tm and spot panchromatic. Photogramm Eng Remote Sens 57, 265–303

  13. Chen T, Zhang J, Zhang Y (2005) Remote sensing image fusion based on ridgelet transform. In Proceedings. 2005 IEEE Int Geosci Remote Sens Symp IGARSS ’05. 2, 1150–1153

  14. Choi J, Yu K, Kim Y (2011) A new adaptive component-substitution-based satellite image fusion by using partial replacement. IEEE Transact Geosci Remote Sens 49(1):295–309

    Article  Google Scholar 

  15. Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. IEEE Transact Image Process 14(12):2091–2106

    Article  Google Scholar 

  16. Easley G, Labate D, Lim WQ (2008) Sparse directional image representations using the discrete shearlet transform. Appl Comput Harmon Anal 25(1):25–46

    Article  MathSciNet  MATH  Google Scholar 

  17. Easley G, Labate D, Lim WQ (2008) Sparse directional image representations using the discrete shearlet transform. Appl Comput Harmon Anal 25(1):25–46

    Article  MathSciNet  MATH  Google Scholar 

  18. Fauvel M, Chanussot J, Benediktsson JA (2006) Decision fusion for the classification of urban remote sensing images. IEEE Transact Geosci Remote Sens 44(10):2828–2838

    Article  Google Scholar 

  19. Gao H (2020) Retracted article: Image denoising based on parallel k-singular value decomposition in cloud computing. Multimed Tools Appl 79(13):9657–9657

    Article  Google Scholar 

  20. Gao R, Vorobyov SA, Zhao H (2017) Image fusion with cosparse analysis operator. IEEE Signal Process Lett 24(7):943–947

    Article  Google Scholar 

  21. Guorong G, Luping X, Dongzhu F (2013) Multi-focus image fusion based on non-subsampled shearlet transform. IET Image Process 7(6):633–639

    Article  Google Scholar 

  22. Huo F, Zhang W, Wang Q, Ren W (2021) Two-stage image denoising algorithm based on noise localization. Multimed Tools Appl

  23. Jin C, Li Q, Jin SW (2019) An adaptive vpde image denoising model based on structure tensor. Multimed Tools Appl 78(19):28331–28354

    Article  Google Scholar 

  24. Jin C, Luan N (2020) An image denoising iterative approach based on total variation and weighting function. Multimed Tools Appl 79(29):20947–20971

    Article  Google Scholar 

  25. Khare A, Khare M, Srivastava R (2021) Shearlet transform based technique for image fusion using median fusion rule. Multimed Tools Appl

  26. Lewis JJ, O’Callaghan RJ, Nikolov SG, Bull DR, Canagarajah N (2007) Pixel- and region-based image fusion with complex wavelets. Information Fusion 8(2):119 – 130. Special Issue on Image Fusion: Advances in the State of the Art

  27. Li S, Kang X, Hu J (2013) Image fusion with guided filtering. IEEE Transact Image Process 22(7):2864–2875

    Article  Google Scholar 

  28. Li S, Yang B (2008) Multifocus image fusion by combining curvelet and wavelet transform. Pattern Recognit Lett 29(9):1295–1301

    Article  Google Scholar 

  29. Li S, Yang B (2010) Hybrid multiresolution method for multisensor multimodal image fusion. IEEE Sens J 10(9):1519–1526

    Article  Google Scholar 

  30. Liu Y, Chen X, Ward RK, Wang ZJ (2016) Image fusion with convolutional sparse representation. IEEE Signal Process Lett 23(12):1882–1886

    Article  Google Scholar 

  31. Liu Y, Liu S, Wang Z (2015) A general framework for image fusion based on multi-scale transform and sparse representation. Info Fusion 24:147–164

    Article  Google Scholar 

  32. Meraoumia A, Chitroub S, Bouridane A (2011) Fusion of finger-knuckle-print and palmprint for an efficient multi-biometric system of person recognition. In 2011 IEEE Int Conf Commun (ICC) 1–5

  33. Nomura K, Sugimura D, Hamamoto T (2018) Underwater image color correction using exposure-bracketing imaging. IEEE Signal Process Lett 25(6):893–897

    Article  Google Scholar 

  34. Papyan V, Elad M (2016) Multi-scale patch-based image restoration. IEEE Transact Image Process 25(1):249–261

    Article  MathSciNet  MATH  Google Scholar 

  35. Paramanandham N, Rajendiran K (2018) Swarm intelligence based image fusion for noisy images using consecutive pixel intensity. Multimed Tools Appl 77:32133–32151

    Article  Google Scholar 

  36. Rahman MM, Antani SK, Thoma GR (2011) A learning-based similarity fusion and filtering approach for biomedical image retrieval using svm classification and relevance feedback. IEEE Transact Info Technol Biomed 15(4):640–646

    Article  Google Scholar 

  37. Redondo R, Sroubek F, Fischer S, Cristobal G (2009) Multifocus image fusion using the log-gabor transform and a multisize windows technique. Info Fusion 10(2):163–171

    Article  Google Scholar 

  38. Sahu S, Singh HV, Kumar B, Singh AK (2019) De-noising of ultrasound image using bayesian approached heavy-tailed cauchy distribution. Multimed Tools Appl 78(4):4089–4106

    Article  Google Scholar 

  39. Shettigara V (1992) A generalized component substitution technique for spatial enhancement of multispectral images using a higher resolution data set. Photogramm Eng Remote Sens 58

  40. Starck JL, Candes EJ, Donoho DL (2002) The curvelet transform for image denoising. IEEE Transact Image Process 11(6):670–684

    Article  MathSciNet  MATH  Google Scholar 

  41. Summers D (2003) Harvard whole brain atlas: https://www.med.harvard.edu/aanlib/home.html. J Neurol Neurosurg Psychiatry 74(3):288

  42. Swathika R, Sharmila TS (2020) Multi-model fusion based satellite image classification using versatile unsupervised vector zone (vuvz) fusion and intensive pragmatic blossoms (ipb) technique. Multimed Tools Appl 79(5):4239–4260

    Article  Google Scholar 

  43. Valsesia D, Fracastoro G, Magli E (2020) Deep graph-convolutional image denoising. IEEE Transact Image Process 29:8226–8237

    Article  MathSciNet  Google Scholar 

  44. Verma R, Pandey R (2018) A statistical approach to adaptive search region selection for nlm-based image denoising algorithm. Multimed Tools Appl 77(1):549–566

    Article  Google Scholar 

  45. Vishwakarma A, Bhuyan M, Iwahori Y (2018) An optimized non-subsampled shearlet transform-based image fusion using hessian features and unsharp masking. J Vis Commun Image Represent 57:48–60

    Article  Google Scholar 

  46. Vishwakarma A, Bhuyan MK (2018) Image fusion using adjustable non-subsampled shearlet transform. IEEE Transact Instrum Meas 1–12

  47. Vishwakarma A, Bhuyan MK (2020) Image mosaicking using improved auto-sorting algorithm and local difference-based harris features. Multimed Tools Appl 79(31):23599–23616

    Article  Google Scholar 

  48. Vishwakarma A, Bhuyan MK, Iwahori Y (2018) Non-subsampled shearlet transform-based image fusion using modified weighted saliency and local difference. Multimed Tools Appl 77(24):32013–32040

    Article  Google Scholar 

  49. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transact Image Process 13(4):600–612

    Article  Google Scholar 

  50. Wu J, An G, Ruan Q (2009) Independent gabor analysis of discriminant features fusion for face recognition. IEEE Signal Process Lett 16(2):97–100

    Article  Google Scholar 

  51. Wu J, Ren X, Xiao Z, Zhang F, Geng L, Zhang S (2020) Research on fundus image registration and fusion method based on nonsubsampled contourlet and adaptive pulse coupled neural network. Multimed Tools Appl 79(47):34795–34812

    Article  Google Scholar 

  52. Xu J, Huang Y, Cheng MM, Liu L, Zhu F, Xu Z, Shao L (2020) Noisy-as-clean: Learning self-supervised denoising from corrupted image. IEEE Transact Image Process 29:9316–9329

    Article  Google Scholar 

  53. Xydeas C, Petrovic V (2000) Objective image fusion performance measure. Electron Lett 36(4):308–309

    Article  Google Scholar 

  54. Yang C, Zhang JQ, Wang XR, Liu X (2008) A novel similarity based quality metric for image fusion. Info Fusion 9(2):156–160

    Article  Google Scholar 

  55. Yang S, Wang M, Jiao L, Wu R, Wang Z (2010) Image fusion based on a new contourlet packet. Info Fusion 11(2):78–84

    Article  Google Scholar 

  56. Yang Y, Que Y, Huang S, Lin P (2016) Multimodal sensor medical image fusion based on type-2 fuzzy logic in nsct domain. IEEE Sens J 16(10):3735–3745

    Article  Google Scholar 

  57. Zhang Q, Guo BL (2009) Multifocus image fusion using the nonsubsampled contourlet transform. Signal Process 89(7):1334 – 1346

  58. Zheng Y, Li M, Zhang J, Wang J (2018) Selection of regularization parameter in gmm based image denoising method. Multimed Tools Appl 77:30121–30134

    Article  Google Scholar 

  59. Zhou X, Wang W, Liu RA (2014) Compressive sensing image fusion algorithm based on directionlets. EURASIP J Wireless Commun Network 2014(1):19

    Article  Google Scholar 

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Authors and Affiliations

  1. Assistant Professor in Discipline of Electronics and Communications Engineering, Pandit Dwarka Prasad Mishra Indian Institute of Information Technology, Design and Manufacturing Jabalpur, Jabalpur, 482005, India

    Amit Vishwakarma

  2. Professor in Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, India

    M. K. Bhuyan

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  1. Amit Vishwakarma
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  2. M. K. Bhuyan
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Correspondence to Amit Vishwakarma.

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Vishwakarma, A., Bhuyan, M.K. A curvelet-based multi-sensor image denoising for KLT-based image fusion. Multimed Tools Appl 81, 4991–5016 (2022). https://doi.org/10.1007/s11042-021-11570-z

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