Brief review of image denoising techniques.

Vis Comput Ind Biomed Art

School of Software, Shandong University, ShunHua Road No.1500, Jinan, 250101, China.

Published: July 2019


Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

With the explosion in the number of digital images taken every day, the demand for more accurate and visually pleasing images is increasing. However, the images captured by modern cameras are inevitably degraded by noise, which leads to deteriorated visual image quality. Therefore, work is required to reduce noise without losing image features (edges, corners, and other sharp structures). So far, researchers have already proposed various methods for decreasing noise. Each method has its own advantages and disadvantages. In this paper, we summarize some important research in the field of image denoising. First, we give the formulation of the image denoising problem, and then we present several image denoising techniques. In addition, we discuss the characteristics of these techniques. Finally, we provide several promising directions for future research.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7099553PMC
http://dx.doi.org/10.1186/s42492-019-0016-7DOI Listing

Publication Analysis

Top Keywords

image denoising
16
denoising techniques
8
image
5
review image
4
denoising
4
techniques explosion
4
explosion number
4
number digital
4
digital images
4
images day
4

Similar Publications

Spatial transcriptomics (ST) reveals gene expression distributions within tissues. Yet, predicting spatial gene expression from histological images still faces the challenges of limited ST data that lack prior knowledge, and insufficient capturing of inter-slice heterogeneity and intra-slice complexity. To tackle these challenges, we introduce FmH2ST, a foundation model-based method for spatial gene expression prediction.

View Article and Find Full Text PDF

The human kidneys play a pivotal role in regulating blood pressure, water, and salt homeostasis, but assessment of renal function typically requires invasive methods. Deuterium metabolic imaging (DMI) is a novel, noninvasive technique for mapping tissue-specific uptake and metabolism of deuterium-labeled tracers. This study evaluates the feasibility of renal DMI at 7-Tesla (7T) to track deuterium-labeled tracers with high spatial and temporal resolution, aiming to establish a foundation for potential clinical applications in the noninvasive investigation of renal physiology and pathophysiology.

View Article and Find Full Text PDF

Concurrent recording of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) signals reveals cross-scale neurovascular dynamics crucial for explaining fundamental linkages between function and behaviors. However, MRI scanners generate artifacts for EEG detection. Despite existing denoising methods, cabled connections to EEG receivers are susceptible to environmental fluctuations inside MRI scanners, creating baseline drifts that complicate EEG signal retrieval from the noisy background.

View Article and Find Full Text PDF

Mass spectrometry imaging (MSI) is a label-free technique that enables the visualization of the spatial distribution of thousands of ions within biosamples. Data denoising is the computational strategy aimed at enhancing the MSI data quality, providing an effective alternative to experimental methods. However, due to the complex noise pattern inherent in MSI data and the difficulty in obtaining ground truth from noise-free data, achieving reliable denoised images remains challenging.

View Article and Find Full Text PDF

Cervical cancer remains a significant cause of female mortality worldwide, primarily due to abnormal cell growth in the cervix. This study proposes an automated classification method to enhance detection accuracy and efficiency, addressing contrast and noise issues in traditional diagnostic approaches. The impact of image enhancement on classification performance is evaluated by comparing transfer learning-based Convolutional Neural Network (CNN) models trained on both original and enhanced images.

View Article and Find Full Text PDF