Denoising Terahertz Image Using Non-Linear Filters
Samuel Akwasi Danso, Shang Liping, Hu Deng, Justice Odoom, Emmsnuel Appiah, Etse Dablu Bobobee, Quancheng Liu · Computer Engineering and Intelligent Systems · 2021
This work was supported by the National Natural Science Foundation of China, (Grant No. 11872058), the Sichuan Science and Technology Program of China (No.2019YFG0114) Abstract A major challenge in the processing and analysis of images is the presence of noise especially in terahertz images. Denoising techniques are designed to remove noise or distorted images while maintaining the original image quality. In this paper, terahertz image denoising is proposed using different filtering methods. This work provides an algorithm that denotes the terahertz image with a non- linear and spatial function of mean and medium filter. The THz images were transformed using Gaussian and Salt & Pepper noise at different percentage levels from 5% to 50%. Universal Image Quantitative Analysis was utilized specifically the most renowned PSNR, MSE, MAE and IEF were explored in this work. Experimental results show that the mean-median filter outperforms Salt and Pepper in removing the Gaussian noise especially when noise factor is increasing. Keywords: Mean- median filter, terahertz image, Gaussian noise, Salt & pepper noise DOI: 10.7176/CEIS/12-2-02 Publication date: April 30 th 2021