【摘要】 为了解决水下图像在复杂水体中表现的画面模糊和颜色失真的问题，提出了一种基于HSV分类、CIELAB均衡与最小卷积区域暗通道先验（DCP）的水下图像恢复算法。基于H与S阈值将水下图像分为高饱和度失真图像、低饱和度失真图像及浅水图像等3类。分类后的水下图像分别经CIELAB均衡及自适应图像增强恢复，其中水下成像系统参数通过最小卷积区域DCP估计。实验结果表明，所提算法在图像恢复效果、评价质量和实时性指标上均优于对比算法，其中峰值信噪比和结构相似指数值分别平均提升了26.88%和17.3%，水下彩色图像质量评价值提升了4.3%。更多还原

【Abstract】 To address the issue of picture blur and color distortion in underwater images of complex water bodies, an underwater image restoration algorithm based on HSV classification, CIELAB equalization, and minimum convolution region dark channel prior(DCP) is proposed. By the thresholds of H and S, the underwater photos are separated into high saturation distortion, low saturation distortion, and shallow water images. Then, the underwater image is recovered using CIELAB equilibrium and adaptive image enhancement, where the system parameters of the categorized underwater image are estimated by minimum convolutional area DCP. The experimental findings demonstrate that the suggested solution is superior to the comparison algorithms in image restoration effect, evaluation quality, and real-time performance indicators.The average peak signal-to-noise ratio and structural similarity values are increased by 26.88% and 17.3% on average,respectively, and the underwater image quality measurement value is increased by 4.3%.更多还原