Image denoising: who is best?

Image denoising: who is best?

Image denoising is a critical task in image processing, particularly in applications where image quality is crucial. In this paper, we compared the performance of five denoising techniques: TV, NLM, BM3D, DnCNN and FFDNet, on grayscale images corrupted with additive white Gaussian noise (AWGN). The...

Full description

Saved in:
Bibliographic Details
Main Author: Yeong, Wei Xian
Other Authors: Qian Kemao
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Online Access:https://hdl.handle.net/10356/165995
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:Image denoising is a critical task in image processing, particularly in applications where image quality is crucial. In this paper, we compared the performance of five denoising techniques: TV, NLM, BM3D, DnCNN and FFDNet, on grayscale images corrupted with additive white Gaussian noise (AWGN). The comparison was based on both quantitative and qualitative evaluation of the various methods. The findings revealed that CNN-based methods outperformed the traditional methods significantly, with FFDNet demonstrating better trade-off between denoising performance and computational complexity. Additionally, several directions for future research were discussed.

Similar Items