EDGE Oriented Image Denoising Through an Adaptive Thresholding in the Complex Wavelet Domain
B.Chinna Rao1, M.Madhavilatha2

1B. Chinna Rao, Associate Professor, Department of Electronics and Communication Engineering, MLR Institute of Technology, Hyderabad, Telangana, India.

2Dr. M. Madhavi Atha, Professor, Department of Electronics and Communication Engineering, JNTU College of Engineering, Hyderabad, Telangana, India.

Manuscript received on 05 March 2019 | Revised Manuscript received on 12 March 2019 | Manuscript Published on 20 March 2019 | PP: 331-339 | Volume-8 Issue- 4S2 March 2019 | Retrieval Number: D1S0074028419/2019©BEIESP

Open Access | Editorial and Publishing 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 (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Noise reduction is a fundamental process in the enhancement of image quality. In recent years, large bodies of approaches have been developed to minimize the effect of noise in the image based applications. In this study, the authors proposed a novel image denoising framework based on applying adaptive thresholding on complex wavelet transform methods. In the proposed approach, the adaptive thresholding has high capacity to tune its parameters according to the noise type and noise intensity. Further, focusing over the preservation of edges with minimum complexity, this paper proposed a new patch grouping mechanism based on the Gabor wavelet coefficients. Simulation experiments are employed over the image samples to evaluate the performance of proposed mechanism by quantifying the signal strength, structural preservation and edge preservation with respect to the PSNR, SSIM and FOM. In the experiments, the proposed approach had shown an optimal performance in both the edge preservation and quality enhancement with less computational burden.

Keywords: Image Denoising, DT-CWT, Gabor Filter, Bayesian Shrink, PSNR, SSIM, FOM.
Scope of the Article: Communication