A Hybrid Image Denoising Method Based on Discrete Wavelet Transformation with Pre-Gaussian Filtering

Nitin; Satinder Bal Gupta

doi:10.17485/IJST/v15i43.1570

Article

A Hybrid Image Denoising Method Based on Discrete Wavelet Transformation with Pre-Gaussian Filtering

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i43.1570

Year: 2022, Volume: 15, Issue: 43, Pages: 2317-2324

Original Article

A Hybrid Image Denoising Method Based on Discrete Wavelet Transformation with Pre-Gaussian Filtering

Nitin^1*, Satinder Bal Gupta²

¹Research Scholar, Department of Computer Science and Engineering, Indira Gandhi University, Meerpur, Rewari, Haryana, India
²Professor, Department of Computer Science and Engineering, Indira Gandhi University, Meerpur, Rewari, Haryana, India

*Corresponding Author
Email: [email protected]

Received Date:29 July 2022, Accepted Date:10 October 2022, Published Date:17 November 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background/Objectives: At the time of acquisition and transmission noise is embedded with the images. It introduces new but unwanted information (noise) in images. The elimination of noise to analyze such data is an essential step in preprocessing. The purpose of this study is to propose a novel image denoising approach to recover original images at high noise densities without introducing unwanted artifacts. Methods: A new hybrid method based on approximation subband thresholding with pre-Gaussian filtering is presented in this study. Google Colab as a platform and python as a programming language is used for the implementation of the proposed technique. To evaluate the performance Peak Signal to Noise Ratio (PSNR) is chosen. The standard jpeg images (Cameraman, Lena, Astronaut, Cat) have been taken as an input and random noise with different noise ratios (s =0.05,0.20,0.30,0.50) is applied to get the noisy images for the experiment. In random noise scenarios, the proposed method experimented on different grayscale standard images, and performance is compared with different existing methods. Findings: The standard images with different noise ratios are denoised by the proposed method, and the quality of images is calculated in terms of PSNR. The results obtained from the proposed method on different standard images improve PSNR (PSNR= 25.80dB, s =0.50) at high noise levels significantly. Novelty: Gaussian filter improve the quality of images. However, when wavelet decomposition is blended with filtered image and thresholding is applied on approximation band improved the quality of images. Hence, the proposed method has a wide area of application to improve image quality in the field of character recognition, agriculture, medical science, and remote sensing.

Keywords: Gaussian Filter; Discrete Wavelet Thresholding; Image denoising; Image Processing

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Copyright

© 2022 Nitin & Gupta. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)