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王永鑫, 刁鸣, 韩闯. 最小二乘估计的水下图像恢复算法[J]. 计算机辅助设计与图形学学报, 2018, 30(11): 2125-2133. DOI: 10.3724/SP.J.1089.2018.17041
引用本文: 王永鑫, 刁鸣, 韩闯. 最小二乘估计的水下图像恢复算法[J]. 计算机辅助设计与图形学学报, 2018, 30(11): 2125-2133. DOI: 10.3724/SP.J.1089.2018.17041
Wang Yongxin, Diao Ming, Han Chuang. Underwater Image Restoration Algorithm Based on Least Squares Estimation[J]. Journal of Computer-Aided Design & Computer Graphics, 2018, 30(11): 2125-2133. DOI: 10.3724/SP.J.1089.2018.17041
Citation: Wang Yongxin, Diao Ming, Han Chuang. Underwater Image Restoration Algorithm Based on Least Squares Estimation[J]. Journal of Computer-Aided Design & Computer Graphics, 2018, 30(11): 2125-2133. DOI: 10.3724/SP.J.1089.2018.17041

最小二乘估计的水下图像恢复算法

Underwater Image Restoration Algorithm Based on Least Squares Estimation

  • 摘要: 为了解决由噪声以及散射所引起的水下图像退化问题,提出一种基于最小二乘估计的水下图像恢复算法.首先通过比尔-朗伯定律构建水下图像退化模型,并基于同态子块的局部统计特性分析方法对于加性高斯白噪声的方差进行估计;然后推导出一种基于最小二乘估计的图像滤波方法用于重建原始图像,通过Gamma校正对由Retinex模型分解出的光照层分量进行拉伸,从而得到增强后的水下图像.从视觉感知和客观评价2个方面对算法进行了验证,实验结果表明,该算法能够有效地抑制由噪声以及散射所引起的图像雾化效果,图像恢复后水下图像的色彩、对比度、细节以及清晰度都得到明显改善.

     

    Abstract: In order to improve the underwater image quality which is degraded by noise and scattering,this paper proposes an underwater image restoration algorithm based on least squares estimation.This work establishes an underwater image degradation model by Beer-Lambert law.The variance of additive white Gaussian noise is estimated by making an analysis on local statistical characteristics of homogeneous patch.This work derives an image filter method based on least squares estimation which is used to restore the original image.The enhanced underwater image is obtained by applying Gamma correction to stretch the light layer component which is decomposed by Retinex model.The proposed algorithm is validated in both aspects of visual perception and objective assessment.Experimental results show that the proposed algorithm is capable to restrain the blurred effect.The quality of underwater restored image is obviously enhanced in terms of color,contrast,detail and clarity.

     

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