A Performance Analysis of Image De-noising Techniques

Abstract

Image de-noising is a serious challenge in image processing techniques. Noise is broadly categorised as additive and multicative noises. In this paper we will discuss about Gaussian noise, salt and pepper noise, speckle noise and poisson noise. Naturally Images contain some amount of noise. But it’s very difficult to denoise it. So blind deconvolution is used. Therefore simulated noise is added to the image to understand the process of denoising. Because after the addition of above mentioned noises if any other transformation or processing is done to the image then the corresponding noises will also be changed. There we will face difficulties to denoise if you don't know the type of noise. So a known noise is added to the image before processing. now let us come to the denoising methods, Out of different available method wavelet thresholding method is one of the important approaches for image de-noising. In this paper we propose an adaptive method of image de-noising in the wavelet sub-band domain assuming the images to be contaminated with noise based on threshold estimation for each sub-band. Under this framework the proposed technique estimates the threshold level by applying sub-band of each decomposition level. This paper details the development of wavelet decomposition on the MATLAB platform. The experimental result reveals that transform domain is better than the spatial domain which we can prove and analyze through the quality metrics factors.

Authors and Affiliations

R. Sindhuja, DR. S. Srinivasan

Keywords

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  • EP ID EP431545
  • DOI 10.9790/9622-0812027882.
  • Views 136
  • Downloads 0

How To Cite

R. Sindhuja, DR. S. Srinivasan (2018). A Performance Analysis of Image De-noising Techniques. International Journal of engineering Research and Applications, 8(12), 78-82. https://europub.co.uk/articles/-A-431545