Traditional and deep-learning-based denoising methods for medical images

作     者：El-Shafai, Walid El-Nabi, Samy Abd Ali, Anas M. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. 

作者机构：Prince Sultan Univ Secur Engn Lab Comp Sci Dept Riyadh 11586 Saudi Arabia Menoufia Univ Fac Elect Engn Dept Elect & Elect Commun Engn Menoufia 32952 Egypt King Salman Int Univ KSIU Fac Comp Sci & Engn Dept Artificial Intelligence Engn South Sinai 46511 Egypt Prince Sultan Univ Robot & Internet Things Lab Riyadh 12435 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia 

出 版 物：《MULTIMEDIA TOOLS AND APPLICATIONS》 (Multimedia Tools Appl)

年 卷 期：2023年第83卷第17期

页      面：52061页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Image denoising Medical images Deep Learning (DL) Spatial filters Wavelet transform Autoencoder 

摘      要：Visual information is extremely important in today s world. Visual information transmitted in the form of digital images has become a critical mode of communication. As a result, digital image processing plays a critical role in advancing the image-related applications. Especially, in the medical field, the image processing stage is one of the important stages that need great accuracy to diagnose and determine the type of the disease. Its objective is to overcome the noise problems in medical images and preserve information and edges in images. Medical images can be enhanced by removing noise through the use of traditional and Deep Learning (DL) methods. DL methods depending on Convolutional Neural Networks (CNNs) have achieved great results in the processing stage for noise reduction in medical images. The DL is a promising and effective solution for estimating real noise and extracting representative features from images. This paper presents a review of image denoising methods for medical images, considering noise sources, and types of noise. The concepts of noise reduction (denoising) for various methods are presented. In addition, a comparative study is presented to clarify the advantages and disadvantages of each method. Finally, some possible trends for future work are introduced.