Abstract
The optical images collected by remotely operated vehicles (ROV) contain a lot of information about underwater (such as distributions of underwater creatures and minerals), which plays an important role in ocean exploration. However, due to the absorption and scattering characteristics of the water medium, some of the images suffer from serious color distortion. These distorted color images usually need to be enhanced so that we can analyze them further. However, at present, no image enhancement algorithm performs well in any scene. Therefore, in order to monitor image quality in the display module of ROV, a no-reference image quality predictor (NIPQ) is proposed in this paper. A unique property that differentiates the proposed NIPQ metric from existing works is the consideration of the viewing behavior of the human visual system and imaging characteristics of the underwater image in different water types. The experimental results based on the underwater optical image quality database (UOQ) show that the proposed metric can provide an accurate prediction for the quality of the enhanced image.
Highlights
There have been a growing number of oceanrelated activities, such as aquaculture, hydrological exploration, and underwater archaeology. e optical images collected by the observational remotely operated vehicle (ROV) provide very convenient conditions for these activities, and high-quality underwater images play an essential role in these activities
(d) We propose NR image quality assessment (IQA)-based underwater smart image display module, which embodies the role of our IQA in application
In order to avoid the evaluation bias caused by prior knowledge, none of the volunteers had the experience of image quality assessment
Summary
There have been a growing number of oceanrelated activities, such as aquaculture, hydrological exploration, and underwater archaeology. e optical images collected by the observational remotely operated vehicle (ROV) provide very convenient conditions for these activities, and high-quality underwater images play an essential role in these activities. Inspired by the underwater image formation model, we distinguish the water types (yellow water, green water, and blue water) in the image by the ocean background area of the image and estimate the reasonable range of pixel intensity of ROI in the enhanced underwater image from the perspective of pixels. In this stage, the difference between the reasonable range of pixel intensity and the ROI pixel intensity of the actual enhanced image is used to represent the rationality of the enhanced image, that is, color fidelity.
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