Contrast Enhancement Using Novel White Balancing Parameter Optimization for Perceptually Invisible Images

2020 ◽  
Vol 29 ◽  
pp. 7525-7536
Author(s):  
Mohit Kumar ◽  
Ashish Kumar Bhandari
Keyword(s):  
Contrast Enhancement ◽  
Parameter Optimization ◽  
White Balancing

An Energy Analyser for Contrast Enhancement in STEM

1974 ◽  
Vol 32 ◽  
pp. 434-435
Author(s):  
H.T. Pearce-Percy
Keyword(s):  
Contrast Enhancement ◽  
Bending Angle ◽  
Magnetic Sector ◽  
Conventional Design ◽  
Bending Radius ◽  
Sector Type

Recently an energy analyser of the uniform magnetic sector type has been installd in a 100KV microscope. This microscope can be used in the STEM mode. The sector is of conventional design (Fig. 1). The bending angle was chosen to be 90° for ease of construction. The bending radius (ρ) is 20 cm. and the object and image distances are 42.5 cm. and 30.0 cm. respectively.

Positive correlation between neovascularization degree of carotid atherosclerosis determined by contrast-enhanced ultrasound and level of serum C-reactive protein

VASA ◽  
2015 ◽  
Vol 44 (3) ◽  
pp. 0187-0194 ◽  
Author(s):  
Xiaoni Chang ◽  
Jun Feng ◽  
Litao Ruan ◽  
Jing Shang ◽  
Yanqiu Yang ◽  
...  
Keyword(s):  
Contrast Enhancement ◽  
Rank Correlation ◽  
Artery Stenosis ◽  
Contrast Enhanced Ultrasound ◽  
C Reactive Protein ◽  
Protein Levels ◽  
Reactive Protein ◽  
Contrast Enhanced ◽  
Grade 3 ◽  
Axis View

Background: Neovascularization is one of the most important risk factors for unstable plaque. This study was designed to correlate plaque thickness, artery stenosis and levels of serum C-reactive protein with the degree of intraplaque enhancement determined by contrast-enhanced ultrasound. Patients and methods: Contrast-enhanced ultrasound was performed on 72 carotid atherosclerotic plaques in 48 patients. Contrast enhancement within the plaque was categorized as grade 1, 2 or 3. Maximum plaque thickness was measured in short-axis view. Carotid artery stenosis was categorized as mild, moderate or severe. Results: Plaque contrast enhancement was not associated with the degree of artery stenosis or with plaque thickness. Serum C-reactive protein levels were positively correlated with the number of new vessels in the plaque. C-reactive protein levels increased in the three groups(Grade 1: 3.72±1.79mg/L; Grade 2: 7.88±4.24 mg/L; Grade 3: 11.02±3.52 mg/L), with significant differences among them (F=10.14, P<0.01), and significant differences between each two groups (P<0.05). Spearman’s rank correlation analysis showed that serum C-reactive protein levels were positively correlated with the degree of carotid plaque enhancement (Rs =0.69, P<0.01). Conclusions: The combination of C-reactive protein levels and intraplaque neovascularization detected by contrast-enhanced ultrasound may allow more accurate evaluation of plaque stability.

CONTRAST ENHANCEMENT IN GRAYSCALE DIGITAL IMAGES APPLYING ATOMIC FUNCTIONS IN FUZZY LOGIC

2013 ◽  
Vol 72 (19) ◽  
pp. 1787-1801
Author(s):  
C. M. Vargas-Martinez ◽  
Victor Filippovich Kravchenko ◽  
Vladimir Il'ich Ponomarev ◽  
Juan Carlos Sanchez-Garcia
Keyword(s):  
Fuzzy Logic ◽  
Contrast Enhancement ◽  
Digital Images

Non-Linear Masking based Contrast Enhancement via Illumination Estimation

2018 ◽  
Vol 2018 (13) ◽  
pp. 389-1-389-5
Author(s):  
Soonyoung Hong ◽  
Minsub Kim ◽  
Moon Gi Kang
Keyword(s):  
Contrast Enhancement ◽  
Illumination Estimation ◽  
Non Linear

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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