Abstract
Light absorption and scattering lead to underwater image showing low contrast, fuzzy, and color cast. To solve these problems presented in various shallow-water images, we propose a simple but effective shallow-water image enhancement method - relative global histogram stretching (RGHS) based on adaptive parameter acquisition. The proposed method consists of two parts: contrast correction and color correction. The contrast correction in RGB color space firstly equalizes G and B channels and then re-distributes each R-G-B channel histogram with dynamic parameters that relate to the intensity distribution of original image and wavelength attenuation of different colors under the water. The bilateral filtering is used to eliminate the effect of noise while still preserving valuable details of the shallow-water image and even enhancing local information of the image. The color correction is performed by stretching the ‘L’ component and modifying ‘a’ and ‘b’ components in CIE-Lab color space. Experimental results demonstrate that the proposed method can achieve better perceptual quality, higher image information entropy, and less noise, compared to the state-of-the-art underwater image enhancement methods.
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Acknowledgment
This work was supported by the Program for Professor of Special Appointment (Eastern Scholar at Shanghai Institutions of Higher Learning No. TP2016038, the National Natural Science Foundation of China (NSFC) Grant 61702323, and the Doctoral Research Startup Fund of Shanghai Ocean University A2-0203-17-100322.
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Huang, D., Wang, Y., Song, W., Sequeira, J., Mavromatis, S. (2018). Shallow-Water Image Enhancement Using Relative Global Histogram Stretching Based on Adaptive Parameter Acquisition. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10704. Springer, Cham. https://doi.org/10.1007/978-3-319-73603-7_37
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