  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.
