Comparison of Image Denoising using Traditional Filter and Deep Learning Methods

Project Code :TCMAPY371

Objective

This paper presents a comparison of the image denoising algorithm using peak signal-to-noise ratio (PSNR) between traditional and deep learning methods on Gaussian noise and Salt and pepper noise condition. And also compare PSNR value of deep learning between noise image to noise image (N2N) learning scheme and noise image to clean image (N2C) learning scheme. At end we compare the results of PSNR values.

Abstract

Image technology is often used in our everyday life. It is useful for many applications such as telecommunication system, automation system in self-driving vehicles, surveillance system and medical research area. However, the image can be interrupted by noise from the environment or electric signal that distorts the detail of the image.

We process a comparison of the image denoising algorithm using peak signal-to-noise ratio between traditional and deep learning methods on Gaussian noise and Salt and pepper noise condition. According to the results, deep learning method has PSNR value higher than traditional method and N2C learning scheme has PSNR value higher than N2N learning scheme.

Keywords: Image De-Noising, Deep Learning, Traditional Filter, Gaussian Noise, Salt and Pepper Noise.

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