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Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
Bosse, Sebastian,Maniry, Dominique,Muller, Klaus-Robert,Wiegand, Thomas,Samek, Wojciech IEEE 2018 IEEE TRANSACTIONS ON IMAGE PROCESSING - Vol.27 No.1
<P>We present a deep neural network-based approach to image quality assessment (IQA). The network is trained end-to-end and comprises ten convolutional layers and five pooling layers for feature extraction, and two fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that: 1) with slight adaptations it can be used in a no-reference (NR) as well as in a full-reference (FR) IQA setting and 2) it allows for joint learning of local quality and local weights, i.e., relative importance of local quality to the global quality estimate, in an unified framework. Our approach is purely data-driven and does not rely on hand-crafted features or other types of prior domain knowledge about the human visual system or image statistics. We evaluate the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the LIVE In the wild image quality challenge database and show superior performance to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation shows a high ability to generalize between different databases, indicating a high robustness of the learned features.</P>
Awareness of Risk Factors for Cancer among Omani adults- A Community Based Study
Al-Azri, Mohammed,AL-Rasbi, Khadija,Al-Hinai, Mustafa,Davidson, Robin,Al-Maniri, Abdullah Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.13
Background: Cancer is the leading cause of mortality around the world. However, the majority of cancers occur as a result of modifiable risk factors; hence public awareness of cancer risk factors is crucial to reduce the incidence. The objective of this study was to identify the level of public awareness of cancer risk factors among the adult Omani population. Materials and Methods: A community based survey using the Cancer Awareness Measure (CAM) questionnaire was conducted in three areas of Oman to measure public awareness of cancer risk factors. Omani adults aged 18 years and above were invited to participate in the study. SPPSS (ver.20) was used to analyse the data. Results: A total of 384 participated from 500 invited individuals (response rate =77%). The majority of respondents agreed that smoking cigarettes (320, 83.3%), passive smoking (279, 72.7%) and excessive drinking of alcohol (265, 69%) are risks factors for cancer. However, fewer respondents agreed that eating less fruit and vegetables (83, 21.6%), eating more red or processed meat (116, 30.2%), being overweight (BMI> 25) (123, 32%), doing less physical exercise (119, 31%), being over 70 years old (72, 18.8%), having a close relative with cancer (134, 34.9%), infection with human papilloma virus (HPV) (117, 30.5%) and getting frequent sunburn during childhood (149, 38.8%) are risk factors for cancer. A significant association was found between participant responses and their educational level. The higher the educational level, the more likely that respondents identified cancer risk factors including smoking (p<0.0005), passive smoking (p= 0.007), excessive drinking of alcohol (p<0.0005), eating less fruit and vegetables (p= 0.001) and infection with HPV (p<0.0005). Conclusions: The majority of respondents in this study in Oman were not aware of the common risk factors for cancer. It may be possible to reduce the incidence of cancers in Oman by developing strategies to educate the public about these risk factors.