Image De-Noising and Compression Using Statistical based Thresholding in 2-D Discrete Wavelet Transform

Images are very good information carriers but they depart from their original condition during transmission and are corrupted by different kind of noise. The purpose is to remove the noisy coefficients such that minimum amount of information is lost and maximum amount of noise is suppressed or reduced. We considered Generalized Gaussian distribution for modeling of noise. In the proposed technique, statistical thresholding methods are used for the estimation of threshold value while Bi-orthogonal wavelet has been envisaged for image decomposition and reconstruction. A qualitative and quantitative analysis of thresholding methods on different images shows significant results for statistical thresholding methods based on objective and subjective quality as compared to other de-noising methods.

@article{Mazhar2016,
  title     = {Image De-Noising and Compression Using Statistical based Thresholding in 2-D Discrete Wavelet Transform},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {11},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Qazi Mazhar and Adil Masood Siddique and Imran Touqir and Adnan Ahmad Khan},
  doi       = {10.14569/IJACSA.2016.071140},
  url       = {https://doi.org/10.14569/IJACSA.2016.071140}
}