The haze phenomenon exerts a degrading effect that decreases contrast and causes color shifts in outdoor images and videos. The presence of haze in outdoor images and videos is bothersome, unpleasant, and occasionally, even dangerous. Atmospheric light scattering (ALS) model is widely used to restore hazy images. In this model, two unknown parameters should be estimated: airlight and scene transmission. The quality of dehazed images and video frames considerably depends on those two parameters as well as on the speed and accuracy of the refinement process of the approximated scene transmission, this refinement is necessary to ensure spatial coherency of the output dehazed video. Spatial coherency should be accounted for in order to eliminate flickering artifacts usually noticed when extending single-image dehazing methods to the video scenario. Classic methods typically require high computation capacity in order to dehaze videos in real time. However, when the driver assistance context is considered, these approaches are inappropriate due to the limited resources mobile environments usually have. To address this issue, this study proposes a framework for real-time video dehazing. This framework consists of two stages: single-image dehazing using the bounded transmission (BT) method, which is utilized to dehaze single video frame in real time with high accuracy; and transmission refinement stage using a filter we call controlled Gaussian filter (CGF), which is proposed for the linear and simplified refinement of the scene transmission. To evaluate the proposed framework, three image datasets in addition to two video streams are employed. Experimental results show that the single-image stage in the proposed framework is at least seven times faster than existing methods. In addition, the analysis of variance (ANOVA) test proves that the quality of dehazed images in this stage is statistically similar to or better than those obtained using existing methods. Also, experiments show that the video stage in the proposed framework is capable of real-time video dehazing with better quality than the existing methods.

Electronic medical record (EMR) is currently a popular topic in e-health. EMR includes the health-related information of patients and forms the main factor of e-health applications. Moreover, EMR contains the legal records that are created in the medical centre and ambulatory environments. These records serve as the data source for electronic health record. Although hospitals utilise the EMR system, healthcare professionals experience difficultly in trusting this system. Studies devoted to EMR acceptance in hospitals are lacking, particularly those on the EMR system in the contexts of privacy and security concerns based on multi-criteria perspective. Thus, the current study proposes a decision support examination framework on how individual, security and privacy determinants influence the acceptance and use of EMR. The proposed framework is based on a multi-criteria perspective derived from healthcare professionals in Malaysia as frame of reference. The framework comprises four phases. The sub-factors of individual, security and privacy determinants were investigated in the two initial phases. Thereafter, the sub-factors were identified with uniform multi-criteria perspective to establish a decision matrix. The decision matrix used individual uniform as basis to cluster the sub-factors and user perspectives. Subsequently, a new 'multi-criteria decision-making (MCDM) approach' was adopted. Integrated technique for order of preference by similarity (TOPSIS) and analytic hierarchy process (AHP) were used as bases in employing the MCDM approach to rank each group of factors. K-means clustering was also applied to identify the critical factors in each group. Healthcare professionals in Malaysia were selected as respondents and 100 questionnaires were distributed to those employed in 5 Malaysian public hospitals. A conceptual model adapted from Unified theory of acceptance and use of technology 2 (UTAUT2) was employed to clarify the connection between individual, privacy and security determinants and EMR system acceptance and use in the selected context. After collecting the data sets (363), structural equation modelling was used to analyse data related to EMR acceptance and use. Results are as follows. (1) Five determinants (i.e. data integrity, confidentiality, non-repudiation, facilitating conditions and effort expectancy) exerted an explicit and important positive effect on EMR acceptance and use. (2) Three determinants (i.e. unauthorised, error and secondary use) exerted a direct and significant negative effect on EMR acceptance and use. (3) Three other determinants (i.e. authentication, performance expectancy and habit) insignificantly affected the behavioural intention of healthcare experts in Malaysia to use EMR.

The haze phenomenon exerts a degrading effect that decreases contrast and causes colour shifts in outdoor images. The presence of haze in digital images is bothersome, unpleasant, and, occasionally, even dangerous. The atmospheric light scattering (ALS) model is widely used to restore hazy images. In this model, two unknown parameters should be estimated: airlight and scene transmission, The quality of dehazed images depends considerably on the accuracy of both estimates. Classic methods typically determine airlight based on the brightest pixels in an image. However, in the traffic scene context, this estimate is compromised when other light sources, such as vehicle headlights from the opposite direction, are present. Transmission estimation is usually more complicated. Hence, the complexity of the overall dehazing process is dependent on this estimate. To address this issue, this study proposes a framework for constant-time airlight and linear transmission estimation. This framework consists of two methods: airlight by image integrals (ALII), which is utilized to estimate the airlight value in real time with high accuracy, and bounded transmission (BT), which is proposed for the linear and simplified estimation of transmission maps. To evaluate the proposed framework, three image datasets are used: (1) seven images that are gathered from the works of existing methods (called the global dataset); (2) the synthetic foggy road image database (FRIDA), which is a synthetically generated dataset for simulating different bad weather conditions; and (3) a dataset of images that were extracted from videos in Malaysia (IV-M), which consists of images that were extracted from traffic video sequences, which were captured in various weather conditions from 2014 to 2016 in Malaysia. Experimental results show that the proposed framework is at least seven times faster than existing methods. In addition, the ANOVA test proves that the quality of the dehazed images is statistically similar to or better than the image quality that was achieved using existing methods. (C) 2018 Elsevier Inc. All rights reserved.