Images obtained in an underwater environment are often affected by colour casting and suffer from poor visibility and lack of contrast. In the literature, there are many enhancement algorithms that improve different aspects of the underwater imagery. Each paper, when presenting a new algorithm or method, usually compares the proposed technique with some alternatives present in the current state of the art. There are no studies on the reliability of benchmarking methods, as the comparisons are based on various subjective and objective metrics. This paper would pave the way towards the definition of an effective methodology for the performance evaluation of the underwater image enhancement techniques. Moreover, this work could orientate the underwater community towards choosing which method can lead to the best results for a given task in different underwater conditions. In particular, we selected five well-known methods from the state of the art and used them to enhance a dataset of images produced in various underwater sites with different conditions of depth, turbidity, and lighting. These enhanced images were evaluated by means of three different approaches: objective metrics often adopted in the related literature, a panel of experts in the underwater field, and an evaluation based on the results of 3D reconstructions.
Underwater images usually suffer from poor visibility, lack of contrast and colour casting, mainly due to light absorption and scattering. In literature, there are many algorithms aimed to enhance the quality of underwater images through different approaches. Our purpose was to identify an algorithm that performs well in different environmental conditions. We have selected some algorithms from the state of the art and we have employed them to enhance a dataset of images produced in various underwater sites, representing different environmental and illumination conditions. These enhanced images have been evaluated through some quantitative metrics. By analysing the results of these metrics, we tried to understand which of the selected algorithms performed better than the others. Another purpose of our research was to establish if a quantitative metric was enough to judge the behaviour of an underwater image enhancement algorithm. We aim to demonstrate that, even if the metrics can provide an indicative estimation of image quality, they could lead to inconsistent or erroneous evaluations.
This paper presents a combined subjective and objective evaluation of an application mixing interactive virtual reality (VR) experience with 360° storytelling. The hypothesis that the modern immersive archaeological VR application presenting cultural heritage from a submerged site would sustain high levels of presence, immersion, and general engagement was leveraged in the investigation of the user experience with both the subjective (questionnaires) and the objective (neurophysiological recording of the brain signals using electroencephalography (EEG)) evaluation methods. Participants rated the VR experience positively in the questionnaire scales for presence, immersion, and subjective judgement. High positive rating concerned also the psychological states linked to the experience (engagement, emotions, and the state of flow), and the experience was mostly free from difficulties linked to the accustomization to the VR technology (technology adoption to the head-mounted display and controllers, VR sickness). EEG results are in line with past studies examining brain responses to virtual experiences, while new results in the beta band suggest that EEG is a viable tool for future studies of presence and immersion in VR.
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