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

🤖 Plain-English Summary

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.

🔑 Key Findings

• To deal with these degradation issues, we propose an efficient and robust underwater image enhancement method, called MLLE.
• 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.
• 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.

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