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首页> 外文期刊>Journal of Engineering >Nonuniform Illumination Correction Algorithm for Underwater Images Using Maximum Likelihood Estimation Method
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Nonuniform Illumination Correction Algorithm for Underwater Images Using Maximum Likelihood Estimation Method

机译:最大似然估计的水下图像非均匀照明校正算法

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Scattering and absorption of light is main reason for limited visibility in water. The suspended particles and dissolved chemical compounds in water are also responsible for scattering and absorption of light in water. The limited visibility in water results in degradation of underwater images. The visibility can be increased by using artificial light source in underwater imaging system. But the artificial light illuminates the scene in a nonuniform fashion. It produces bright spot at the center with the dark region at surroundings. In some cases imaging system itself creates dark region in the image by producing shadow on the objects. The problem of nonuniform illumination is neglected by the researchers in most of the image enhancement techniques of underwater images. Also very few methods are discussed showing the results on color images. This paper suggests a method for nonuniform illumination correction for underwater images. The method assumes that natural underwater images are Rayleigh distributed. This paper used maximum likelihood estimation of scale parameter to map distribution of image to Rayleigh distribution. The method is compared with traditional methods for nonuniform illumination correction using no-reference image quality metrics like average luminance, average information entropy, normalized neighborhood function, average contrast, and comprehensive assessment function.
机译:光的散射和吸收是水中可见度有​​限的主要原因。水中的悬浮颗粒和溶解的化合物也负责光在水中的散射和吸收。水中可见度有​​限会导致水下图像质量下降。通过在水下成像系统中使用人造光源,可以提高可见度。但是人造光以不均匀的方式照亮了场景。它在中心产生亮点,在周围产生暗区。在某些情况下,成像系统本身会通过在对象上产生阴影来在图像中创建暗区。在水下图像的大多数图像增强技术中,研究人员忽略了照明不均匀的问题。也很少讨论在彩色图像上显示结果的方法。本文提出了一种对水下图像进行非均匀照度校正的方法。该方法假定自然水下图像是瑞利分布的。本文使用尺度参数的最大似然估计将图像分布映射到瑞利分布。该方法与使用非参考图像质量指标(例如平均亮度,平均信息熵,归一化邻域函数,平均对比度和综合评估函数)的非均匀照明校正的传统方法进行了比较。

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