A Proposal for Quality Enhancement, Diffusion and Progressive Management of Korean Legal Writing & Citation Manual (K-Law Citation Manual)

The Justice ◽  
2021 ◽  
Vol 187 ◽  
pp. 279-320
Author(s):  
Chaho JUNG ◽  
Sung Pill HWANG ◽  
Jielin LIU
Keyword(s):  
Quality Enhancement ◽  
Legal Writing

Quality Enhancement of the Rounded Wood Surface

2017 ◽  
Vol 33 (1) ◽  
Author(s):  
Aleksandr Chemodanov ◽  
Evgenii Iamshchikov ◽  
Roman Lozhkin ◽  
Aleksandr Turuev
Keyword(s):  
Wood Surface ◽  
Quality Enhancement

Interactive Image Quality Enhancement Support System by Tone Curve Correction Based on Human KANSEI

2009 ◽  
Vol 129 (6) ◽  
pp. 593-600 ◽  
Author(s):  
Yuichiro Tokuda ◽  
Gosuke Ohashi ◽  
Masato Tsukada ◽  
Reiichi Kobayashi ◽  
Yoshifumi Shimodaira
Keyword(s):  
Image Quality ◽  
Support System ◽  
Quality Enhancement ◽  
Curve Correction ◽  
Image Quality Enhancement

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.

Power Quality Enhancement using STATCOM with Energy Storage

2014 ◽  
pp. 349-354 ◽  
Author(s):  
A. Yanushkevich ◽  
Z. Müller ◽  
J. Švec ◽  
J. Tlustý ◽  
V. Valouch
Keyword(s):  
Energy Storage ◽  
Power Quality ◽  
Quality Enhancement
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