Automatic Enhancement of Low Light Level Image on the Basis of a Gaussian Mixture Model

Liju Yin, Tingdong Kou, Xuan Wang, Guofeng Zou, Jinfeng Pan, Zhongshang Zhu

Abstract


As the first medium to transmit information under a low light level environment. The low light level image is needed in hot-light image imaging technology. The quality of the image will be reduced given the influence of external factors. For example, a sampled image may become blurry. This paper proposes a method for automatic enhancement of low light level image on the basis of a Gaussian mixture model. First, the histogram of the image is modeled with a Gaussian mixture model that is solved by the expectation maximization algorithm of accelerated convergence. The histogram is then partitioned according to the intersection of each cluster. Finally, the mapping relationship of the cluster to which the output image belongs is ascertained and the final enhancement image is obtained. This algorithm can be used to identify the optimal number of clusters and accelerate the convergence speed of the algorithm. Objective evaluation of the Laplace operator value, as well as the grayscale average gradient and contrast (Tab. 1), indicates that the algorithm effectively improves image contrast while maintaining the details of the image.


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DOI: https://doi.org/10.24203/ajeel.v7i3.5818

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