Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement

Underwater images typically suffer from color deviations and low visibility due to the wavelength-dependent light absorption and scattering. Experiments further suggest that our method can effectively improve the performance of underwater image segmentation, keypoint detection, and saliency detection.

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• 1 To deal with these degradation issues, we propose an efficient and robust underwater image enhancement method, called MLLE.
• 2 Specifically, we first locally adjust the color and details of an input image according to a minimum color loss principle and a maximum attenuation map-guided fusion strategy.
• 3 Afterward, we employ the integral and squared integral maps to compute the mean and variance of local image blocks, which are used to adaptively adjust the contrast of the input image.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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