Dual tree complex wavelet transform based image denoising for Kalpana satellite images

  • Authors

    • Naga Lingamaiah Kurva
    • S Varadarajan
    2018-08-24
    https://doi.org/10.14419/ijet.v7i3.29.18810
  • DTCWT, Gaussian Noise, Noise Reduction, Poisson Noise, PSNR, Salt & Pepper Noise.

  • This paper presents a new algorithm to reduce the noise from Kalpana Satellite Images using Dual Tree Complex Wavelet Transform technique. Satellite Images are not simple photographs; they are pictorial representation of measured data. Interpretation of noisy raw data leads to wrong estimation of geophysical parameters such as precipitation, cloud information etc., hence there is a need to improve the raw data by reducing the noise for better analysis. The satellite images are normally affected by various noises. This paper mainly concentrates on reducing the Gaussian noise, Poisson noise and Salt & Pepper noise. Finally the performance of the DTCWT wavelet measures in terms of Peak Signal to Noise Ratio and Structural Similarity Index for both noisy & denoised Kalpana images.

     

     


     

  • References

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  • How to Cite

    Lingamaiah Kurva, N., & Varadarajan, S. (2018). Dual tree complex wavelet transform based image denoising for Kalpana satellite images. International Journal of Engineering & Technology, 7(3.29), 269-271. https://doi.org/10.14419/ijet.v7i3.29.18810