Underwater Image Color Correction by Complementary Adaptation

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 and an inverse transform of the Helmholtz-Kohlrausch effect as the post-processing. We analyze and validate the proposed model by various numerical experiments. Based on image quality metrics designed for underwater conditions, we compare with some state-of-art approaches to show that the proposed method has consistently superior performances.