Abstract
Underwater image enhancement is necessary to study aquatic flora and fauna. However, due to light absorption and scattering, acquired subaqueous images are gravely hazed and degraded. This results in low contrast of the underwater image. In literature, many algorithms aim to dehaze and enhance an image’s quality. The NLP-GRM method aims to design an efficient algorithm that carries out superior results under a different environmental condition in terms of visual analysis and objective evaluation. Our approach integrates the gradient residual minimization (GRM) and then on local prior to a new method called NLP-GRM. Initially, an underwater image is processed through a nonlocal prior to dehaze the image by color assumption. The nonlocal prior (NLP) output works with robust GRM, reinforcing the edge strength and detail in the image post removal of underwater haze. The execution of the NLP-GRM algorithm has been observed by quantitative metrics and subjectively as well. The experimental results demonstrate that the NLP-GRM is superior to existing underwater enhancement methods.
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Khoond, R., Goyal, B., Dogra, A. (2021). Image Enhancement Using Nonlocal Prior and Gradient Residual Minimization for Improved Visualization of Deep Underwater Image. In: Deshpande, A., Estrela, V.V., Razmjooy, N. (eds) Computational Intelligence Methods for Super-Resolution in Image Processing Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-67921-7_14
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DOI: https://doi.org/10.1007/978-3-030-67921-7_14
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