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

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.