Oil Agglomeration Towards Quality Enhancement of High-Ash Coals: The Indian Scenario

Clean Coal Technologies ◽

10.1007/978-3-030-68502-7_4 ◽

2021 ◽

pp. 71-82

Author(s):

Saswati Chakladar ◽

Ashok Kumar Mohanty ◽

Sanchita Chakravarty

Keyword(s):

Quality Enhancement ◽

Oil Agglomeration ◽

Indian Scenario

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Quality Enhancement Provider Handbook

PsycEXTRA Dataset ◽

10.1037/e536072010-001 ◽

2008 ◽

Keyword(s):

Quality Enhancement

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VA Quality Enhancement Research Initiative for Substance-Use Disorders Treatment

PsycEXTRA Dataset ◽

10.1037/e535192007-001 ◽

2006 ◽

Author(s):

Daniel R. Kivlahan

Keyword(s):

Substance Use ◽

Substance Use Disorders ◽

Quality Enhancement ◽

Quality Enhancement Research Initiative ◽

Research Initiative

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Quality enhancement themes in Scotland

PsycEXTRA Dataset ◽

10.1037/e719462011-009 ◽

2003 ◽

Author(s):

Sarah Tweedie

Keyword(s):

Quality Enhancement

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Indian scenario in solar thermal concentrating technologies

Journal de Physique IV (Proceedings) ◽

10.1051/jp4:1999331 ◽

1999 ◽

Vol 09 (PR3) ◽

pp. Pr3-211-Pr3-216

Author(s):

B. Parthan

Keyword(s):

Solar Thermal ◽

Indian Scenario

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Quality Enhancement of the Rounded Wood Surface

Vestnik of Volga State University of Technology Ser Forest Ecology Nature Management ◽

10.15350/2306-2827.2017.1.78 ◽

2017 ◽

Vol 33 (1) ◽

Author(s):

Aleksandr Chemodanov ◽

Evgenii Iamshchikov ◽

Roman Lozhkin ◽

Aleksandr Turuev

Keyword(s):

Wood Surface ◽

Quality Enhancement

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Cost Variation Study of Antidiabetics: Indian Scenario

Indian Journal Of Applied Research ◽

10.15373/2249555x/may2014/130 ◽

2011 ◽

Vol 4 (5) ◽

pp. 420-421 ◽

Cited By ~ 2

Author(s):

H. N. Lalan H. N. Lalan ◽

◽

Manjusha K. Borde ◽

Ipseeta Mohanty Ray ◽

Y. A. Deshmukh Y. A. Deshmukh

Keyword(s):

Cost Variation ◽

Indian Scenario

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

IEEJ Transactions on Industry Applications ◽

10.1541/ieejias.129.593 ◽

2009 ◽

Vol 129 (6) ◽

pp. 593-600 ◽

Cited By ~ 1

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

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Deep Learning Approaches for Whiteboard Image Quality Enhancement

Color and Imaging Conference ◽

10.2352/j.imagingsci.technol.2019.63.4.040404 ◽

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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