水下核燃料组件湍流图像复原研究
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摘要
核反应堆为保证安全生产,需要在停堆时利用视觉对核燃料组件进行核心验证。由于燃料组件的余热导致水下热湍流,严重降低视频质量,故为获取湍流下燃料组件的清晰图像,本文提出了一种去除热湍流干扰的燃料组件图像清晰化算法,该算法主要包含幸运块时域滤波模块和解卷积模块。首先由幸运块选择构建时域滤波算法器以得到稳定但模糊的图像,然后根据湍流退化模型,再对图像进行解卷积滤波。实验结果显示,本文算法能很好地对热湍流图像进行清晰化。
In order to ensure safe production,nuclear reactors need to use the vision to core verification of nuclear fuel components during shutdown. Due to the residual heat of the fuel assembly,the underwater heat turbulence is caused,which seriously degrades the video quality. Therefore,in order to obtain a clear image of the fuel component under the turbulence,this paper proposes a fuel component image sharpening algorithm that removes thermal turbulence interference. The algorithm mainly includes the lucky region time domain filtering module and the deconvolution module. First,the time domain filtering algorithm is constructed by the lucky block to obtain a stable but blurred image,and then the image is deconvoluted and filtered according to the turbulence degradation model. The experimental results show that the proposed algorithm can clearly clear the hot turbulence image.
In order to ensure safe production,nuclear reactors need to use the vision to core verification of nuclear fuel components during shutdown. Due to the residual heat of the fuel assembly,the underwater heat turbulence is caused,which seriously degrades the video quality. Therefore,in order to obtain a clear image of the fuel component under the turbulence,this paper proposes a fuel component image sharpening algorithm that removes thermal turbulence interference. The algorithm mainly includes the lucky region time domain filtering module and the deconvolution module. First,the time domain filtering algorithm is constructed by the lucky block to obtain a stable but blurred image,and then the image is deconvoluted and filtered according to the turbulence degradation model. The experimental results show that the proposed algorithm can clearly clear the hot turbulence image.
引文
[1]董毅漫,李光辉,曲云欢.基于核安全的核能发展重点问题[J].环境保护,2017(18):36~40.
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[5]陈前荣,陆启生,成礼智,等.利用拉氏算子鉴别散焦模糊图像点扩散函数[J].计算机工程与科学,2005,27(9):40~43.
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[7]赵桂青.图像的盲解卷积恢复[J].微计算机信息,2007,23(6):278~279.
[2]Wen Z,Lambert A,Fraser D,et al.Bispectral analysis and recovery of images distorted by a moving water surface[J].Applied Optics,2010,49(33):6376.
[3]蒋刚毅,黄大江,王旭.图像质量评价方法研究进展[J].电子与信息学报,2010,32(1):219~226.
[4]Zhang L,Zhang L,Mou X,et al.FSIM:a feature similarity index for image quality assessment[J].IEEE Transactions on Image Processing APublication of the IEEE Signal Processing Society,2011,20(8):2378.
[5]陈前荣,陆启生,成礼智,等.利用拉氏算子鉴别散焦模糊图像点扩散函数[J].计算机工程与科学,2005,27(9):40~43.
[6]张德丰,张葡青.维纳滤波图像恢复的理论分析与实现[J].中山大学学报(自然科学版),2006,45(6):44~47.
[7]赵桂青.图像的盲解卷积恢复[J].微计算机信息,2007,23(6):278~279.