New Image Denoising Algorithms
Author: Mickael Aghajarian
Publisher:
Published: 2021
Total Pages: 83
ISBN-13:
DOWNLOAD EBOOKImage noise is unwanted fluctuations in pixel intensities that is often inevitable during the process of acquisition, compression, and transmission for many reasons such as imperfections in capturing instruments, limitations of analog-to-digital converters, and interference in transmission channels. The existence of noise not only degrades the visual quality of images but also adversely affects the performance of image processing and computer vision tasks, such as classification, detection, and segmentation. Thus, removing or attenuating the effect of image noise (i.e., image denoising) is often an essential preprocessing task in the field of image processing and computer vision. In this dissertation, we addressed three image noises: salt-and-pepper (SAP) impulse noise, random-valued impulse noise (RVIN), and Gaussian noise. In order to restore images contaminated by SAP impulse noise, we used the modified mean filter and total variation of corrupted pixels that was minimized by using convex optimization. For RVIN, we implemented a three-step method that restored images by estimating the noise density, detecting the noisy pixels, and applying a modified weighted mean filter to the detected noisy pixels. For images corrupted by Gaussian noise, we proposed a deep convolutional neural network that handled a wide range of noise levels by using two trained models, one for low noise and the other for high noise levels. We compared the performance of our noise removal methods with other state-of-the-art algorithms and in the vast majority of the cases, our method outperformed other image denoising algorithms which showed the effectiveness of the proposed methods.
