Implementation Of Spatial-Scale Domain Based De-Noising Techniques using Different Thresholding
Dawar Husain1, Upendra kumar2, Monauwer Alam3

1Dawar Husain*, Department of Electronics A.I.M.T Lucknow.
2Upendra Kumar, Department of Electronics A.I.M.T Lucknow.
3Monauwer Alam, Department of Electrical Engg. Integral University, Lucknow.
Manuscript received on December 13, 2019. | Revised Manuscript received on December 24, 2019. | Manuscript published on January 10, 2020. | PP: 3594-3603 | Volume-9 Issue-3, January 2020. | Retrieval Number: B7367129219/2020©BEIESP | DOI: 10.35940/ijitee.B7367.019320
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: This paper aims in presenting a thorough comparison of performance and usefulness of multi-resolution based de-noising technique. Multi-resolution based image denoising techniques overcome the limitation of Fourier, spatial, as well as, purely frequency based techniques, as it provides the information of 2-Dimensional (2-D) signal at different levels and scales, which is desirable for image de-noising. The multiresolution based de-noising techniques, namely, Contourlet Transform (CT), Non Sub-sampled Contourlet Transform (NSCT), Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT), have been selected for the de-noising of camera images. Further, the performance of different denosing techniques have been compared in terms of different noise variances, thresholding techniques and by using well defined metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Analysis of result shows that shift-invariant NSCT technique outperforms the CT, SWT and DWT based de-noising techniques in terms of qualititaive and quantitative objective evaluation. 
Keywords: Image De-noising, Multi-resolution Domain Filtering, Non Sub-sampled Contourlet Transform
Scope of the Article: Image analysis and Processing