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

TLDR

Underwater images suffer from color deviations and low visibility due to wavelength‑dependent light absorption and scattering. The authors propose MLLE, an efficient and robust underwater image enhancement method to address these degradation issues. MLLE locally adjusts color and details using a minimum color loss principle and an attenuation‑map guided fusion, computes local mean and variance via integral maps to adaptively adjust contrast, and applies a CIELAB color‑balance strategy to equalize channel a and b differences. The method yields vivid color, improved contrast, and enhanced details, outperforms state‑of‑the‑art on three datasets, runs in under 1 s on a single CPU, and improves downstream tasks such as segmentation, keypoint detection, and saliency detection. Project page: https://li-chongyi.github.io/proj_MMLE.html.

Abstract

Underwater images typically suffer from color deviations and low visibility due to the wavelength-dependent light absorption and scattering. 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. Meanwhile, a color balance strategy is introduced to balance the color differences between channel a and channel b in the CIELAB color space. Our enhanced results are characterized by vivid color, improved contrast, and enhanced details. Extensive experiments on three underwater image enhancement datasets demonstrate that our method outperforms the state-of-the-art methods. Our method is also appealing in its fast processing speed within 1s for processing an image of size 1024×1024×3 on a single CPU. Experiments further suggest that our method can effectively improve the performance of underwater image segmentation, keypoint detection, and saliency detection. The project page is available at https://li-chongyi.github.io/proj_MMLE.html.