Underwater Image Color Correction by Complementary Adaptation
Yuchen He

TL;DR
This paper introduces a novel underwater image color correction method based on a Tikhonov optimization model in the CIELAB space, inspired by psychophysical complementary adaptation theory, effectively removing color casts and enhancing image quality.
Contribution
It presents a new variational approach linking color constancy and human visual system adaptation, with robust hue-selective enhancement for diverse underwater environments.
Findings
Outperforms state-of-the-art methods in underwater image quality metrics
Effectively removes underwater color cast and balances colors
Enhances contrast with hue-selective, image-based rescaling
Abstract
In this paper, we propose a novel approach for underwater image color correction based on a Tikhonov type optimization model in the CIELAB color space. It presents a new variational interpretation of the complementary adaptation theory in psychophysics, which establishes the connection between colorimetric notions and color constancy of the human visual system (HVS). Understood as a long-term adaptive process, our method effectively removes the underwater color cast and yields a balanced color distribution. For visualization purposes, we enhance the image contrast by properly rescaling both lightness and chroma without trespassing the CIELAB gamut. The magnitude of the enhancement is hue-selective and image-based, thus our method is robust for different underwater imaging environments. To improve the uniformity of CIELAB, we include an approximate hue-linearization as the pre-processing…
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Taxonomy
TopicsImage Enhancement Techniques · Color Science and Applications · Advanced Image Fusion Techniques
