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Denoising of Multispectral Images: An Adaptive Approach

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International Conference on Intelligent and Smart Computing in Data Analytics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1312))

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Abstract

Spectral imaging enables detection of external details not processed by the human eye through its red, green, and blue receptors. Multispectral imaging aims to collect the range in a scene picture for each pixel and provide more accurate detail. Different noises eventually compromise Multispectral Image (MSI) due to hardware constraints and insufficiency in radiance. Hence, to enhance the quality of image, we proposed Kriging Interpolation-based Wiener Filtering (KIWF). This makes use of kriging interpolation algorithm to calculate the weights of wiener filter so that the best possible estimate is obtained for denoising the image. Initially, the pixels with noise are separated from clear pixels by global patch clustering, and the weight values are applied by estimating the semi-variance between the clear patches and noisy patches. Finally, the performance of the filter is tested and a comparative analysis is conducted with the existing denoising techniques to show its effectiveness.

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References

  1. Wu X, Zhou B, Ren Q et al (2020) Multispectral image denoising using sparse and graph Laplacian Tucker decomposition. Comp Visual Media 6:319–331

    Article  Google Scholar 

  2. Kong Z, Yang, X.: Color image and multispectral image denoising using block diagonal representation. IEEE Trans Image Process

    Google Scholar 

  3. Karami A, Tafakori L (2017) Image denoising using generalized Cauchy filter. Image Process IET 11(9):767–776

    Article  Google Scholar 

  4. Van Beers WCM, Kleijnen JPC (2003) Kriging for interpolation in random simulation. J Oper Res Soc 54:255–262

    Google Scholar 

  5. Zeng S, Huang R, Kang X, Sang N (2014) Image segmentation using spectral clustering of Gaussian mixture models. Neuro Comput 346–356

    Google Scholar 

  6. Starck JL, Candes E, Donoho D (2002) The curvelet transform for image denoising. IEEE Trans Image Process 11(6):670–684

    Google Scholar 

  7. Deng G, Liu Z (2015) A wavelet image denoising based on the new threshold function. In: 2015 11th international conference on computational intelligence and security (CIS), Dec 19. IEEE, pp 158–161

    Google Scholar 

  8. Jassim FA (2013) Kriging interpolation filter to reduce high density salt and pepper noise. World Comput Sci Inform Technol J 3:8–14

    Google Scholar 

  9. 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

    Article  MathSciNet  MATH  Google Scholar 

  10. Knaus C, Zwicker M (2014) Progressive image denoising. IEEE Trans Image Process 23(7):3114–3125

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Dr. A.P.J. Abdul Kalam University, Indore, Madhya Pradesh, India

    P. Lokeshwara Reddy & Santosh Pawar

  2. University of Technology and Applied Sciences, Al Musannah, Oman

    Kanagaraj Venusamy

Authors
  1. P. Lokeshwara Reddy
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  2. Santosh Pawar
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  3. Kanagaraj Venusamy
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Corresponding author

Correspondence to P. Lokeshwara Reddy .

Editor information

Editors and Affiliations

  1. Rajnagar Mahavidyalaya, Rajnagar, Birbhum, India

    Siddhartha Bhattacharyya

  2. Department of Computer Science and Engineering, Aditya Institute of Technology and Management (AITAM), Srikakulam, India

    Janmenjoy Nayak

  3. Department of Computer Science and Engineering, K. L. University, Vijayawada, India

    Kolla Bhanu Prakash

  4. Department of Computer Applications, Veer Surendra Sai University of Technology, Sambalpur, India

    Bighnaraj Naik

  5. Machine Intelligence Research Labs, Auburn, AL, USA

    Ajith Abraham

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© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Lokeshwara Reddy, P., Pawar, S., Venusamy, K. (2021). Denoising of Multispectral Images: An Adaptive Approach. In: Bhattacharyya, S., Nayak, J., Prakash, K.B., Naik, B., Abraham, A. (eds) International Conference on Intelligent and Smart Computing in Data Analytics. Advances in Intelligent Systems and Computing, vol 1312. Springer, Singapore. https://doi.org/10.1007/978-981-33-6176-8_22

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