People with color vision deficiency (CVD) have difficulty discriminating colors, which can cause loss of chromatic contrast in the perception of affected individuals. To compensate for contrast loss, image recoloring approaches have been proposed in the existing studies. In state-of-the-art studies, recoloring models were built based on an approximated gamut of CVD in the CIE L*a*b* (Lab) color space, which significantly deviates from the original gamut. In addition, luminance was not considered during recoloring. Moreover, existing methods also present problems, such as high computational costs and insufficient naturalness preservation . In this paper, we propose a novel recoloring method to compensate for CVD that enhances contrast through adopting a luminance channel-considered optimization model while preserving naturalness by imposing hard constraints on the amount of changes to the original colors. To obtain a better compensation effect, we fit a new curved surface for representing the gamut of dichromacy in the Lab color space more accurately. Furthermore, a discrete solver is implemented to solve the optimization problem efficiently. For effective assessment, qualitative, quantitative, and subjective experiments were conducted, and a new metric, called preference, is proposed to evaluate the contrast enhancement and naturalness preservation comprehensively.

患有色觉缺陷 (CVD) 的人难以辨别颜色，这会导致受影响个体的感知失去色彩对比。为了补偿对比度损失，现有研究中已经提出了图像重新着色方法。在最先进的研究中，重新着色模型是基于 CIE L*a*b* (Lab) 颜色空间中的近似 CVD 色域构建的，该色域与原始色域显着偏离。此外，在重新着色期间没有考虑亮度。此外，现有方法也存在计算成本高、自然度保存不足等问题。在本文中，我们提出了一种新的重新着色方法来补偿 CVD，该方法通过采用亮度通道考虑的优化模型来增强对比度，同时通过对原始颜色的变化量施加硬约束来保持自然度。为了获得更好的补偿效果，我们拟合了一个新的曲面来更准确地表示 Lab 颜色空间中的二色色域。此外，实现了离散求解器以有效地解决优化问题。为了进行有效的评估，进行了定性、定量和主观实验，并提出了一种称为偏好的新指标来综合评估对比度增强和自然度保持。我们拟合了一个新的曲面，以更准确地表示 Lab 颜色空间中的二色色域。此外，实现了离散求解器以有效地解决优化问题。为了进行有效的评估，进行了定性、定量和主观实验，并提出了一种称为偏好的新指标来综合评估对比度增强和自然度保持。我们拟合了一个新的曲面，以更准确地表示 Lab 颜色空间中的二色色域。此外，实现了离散求解器以有效地解决优化问题。为了进行有效的评估，进行了定性、定量和主观实验，并提出了一种称为偏好的新指标来综合评估对比度增强和自然度保持。