New Attenuation Map for SPECT Images Quality Enhancement

2018 ◽  
Author(s):  
Hamida Romdhane ◽  
Mohamed Ali Cherni ◽  
Dorra Ben Sellem
Keyword(s):  
Quality Enhancement ◽  
Attenuation Map ◽  
Spect Images

Effects of collimator dependency and correction methods on I-123 SPECT images using x-ray-based attenuation map

2006 ◽  
Author(s):  
Yu-Wen Yang ◽  
Jyh-Cheng Chen ◽  
Chia-Jung Chang ◽  
Cheng-Yi Cheng ◽  
Wen-Sheng Huang
Keyword(s):  
X Ray ◽  
Attenuation Map ◽  
Spect Images

scholarly journals Effects of attenuation map accuracy on attenuation-corrected micro-SPECT images

2013 ◽  
Vol 3 (1) ◽  
pp. 7 ◽  
Author(s):  
Chao Wu ◽  
Hugo A Gratama van Andel ◽  
Peter Laverman ◽  
Otto C Boerman ◽  
Freek J Beekman
Keyword(s):  
Attenuation Map ◽  
Attenuation Corrected ◽  
Map Accuracy ◽  
Spect Images

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.

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