摘要
Visual inspection of the key components of nuclear power plants(NPPs)is important for NPP operation and maintenance. However,the underwater environment and existing radiation will lead to image degradation,thus making it difficult to identify surface defects. In this study,a method for improving the quality of underwater images is proposed.By analyzing the degradation characteristics of underwater detection image,the image enhancement technology is used to improve the color richness of the image,and then the improved dark channel prior(DCP)algorithm is used to restore it. By modifying the estimation formula of transmittance and background light,the correction of insufficient brightness in DCP restored image is realized. The proposed method is compared with other state-of-the-art methods. The results show that the proposed method can achieve higher scores and improve the image quality by correcting the color and restoring local details,thus effectively enhancing the reliability of visual inspection of NPPs.
核电站(Nuclear power plants,NPPs)水下视觉检测时会由于特殊的环境导致检测图像退化。为提高视觉检测能力,本文通过分析检测图像的退化特性,提出一种融合图像增强与图像复原算法的水下图像质量增强方法。首先采用图像增强技术,以改善图像的色彩丰富度;其次采用暗通道先验算法,消除图像雾化的影响。针对暗通道先验(Dark channel prior,DCP)算法处理水下图像时容易导致整体亮度不足的问题,通过修正透射率与背景光估计校正亮度,进而提升水下检测图像的质量。通过构建一个真实水下检测图像的数据集,对提出方法进行验证,结果表明:本文方法通过颜色校正和局部细节复原有效提高了图像质量,从而实现提升核电站视觉检测的可靠性;与其他算法对比分析的试验进一步表明,本文方法能够获得更优越的性能。
基金
supported by the National Natural Science Foundations of China (Nos. 51674031,51874022)。
