论文标题
水下图像通过互补适应的颜色校正
Underwater Image Color Correction by Complementary Adaptation
论文作者
论文摘要
在本文中，我们提出了一种基于Cielab色彩空间中Tikhonov型优化模型的水下图像颜色校正的新方法。它提出了对心理物理学补充适应理论的新变化解释，该解释建立了人类视觉系统（HVS）的比色概念和颜色稳定性之间的联系。我们的方法理解为长期自适应过程，我们的方法有效地去除了水下颜色，并产生平衡的颜色分布。出于可视化目的，我们通过在不侵入cielab范围的情况下正确重新缩放轻度和色度来增强图像对比度。增强的大小是色相选择性和基于图像的幅度，因此我们的方法适合不同的水下成像环境。为了提高CIELAB的均匀性，我们将近似色调线性化作为预处理和Helmholtz-Kohlrausch效应的逆变换作为后处理。我们通过各种数值实验来分析和验证所提出的模型。基于为水下条件设计的图像质量指标，我们将其与某些最新方法进行比较，以表明所提出的方法始终具有出色的性能。
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.
