基于双域分解的矿井下图像增强算法

为提高矿井下图像的对比度, 并同步地抑制图像的雾尘和噪声, 提出一种基于双域分解的矿井下图像增强算法.首先, 采用双边滤波器将输入图像分解为低频图像和高频图像; 其次, 采用快速暗原色去雾算法和Gamma变换, 实现低频图像的去雾和对比度提高; 接着, 采用非下采样Shearlet变换和二阶微分算子, 实现高频图像降噪和增强; 最后, 将增强的低频、高频图像合成基础增强图像, 并抑制粉尘散射模糊和过曝光白色伪影, 得到最终增强图像.实验表明, 该方法不仅能有效提高矿井下图像的对比度, 还能有效抑制图像的雾气和噪声, 具有广泛的应用前景.

Abstract

To better the contrast of the images under the mine and suppress the influence of dust and noise, a mine image enhancement algorithm based on dual domain decomposition is proposed. Firstly, input images are decomposed into low frequency images and high frequency images respectively using bilateral filter. Secondly, haze removal and contrast enhancement of the low frequency images are realized by using the fast-dark channel prior dehazing algorithm and gamma transform. After that, non-subsampled Shearlet transform and second order differential operator are adopted to realize the high-frequency image denoising and enhancement. At last, the enhanced high-frequency and low-frequency images are composited into basic enhanced images followed by suppression of dust blur and over-exposure white artifact to obtain the desirable enhanced images. Result of the experiment shows that our algorithm is widely applicable for it not only can enhance the contrast of the images under the mine but also suppress the haze and noise effectively.

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田子建, 王满利, 吴君, 桂伟峰, 王文清. 基于双域分解的矿井下图像增强算法[J]. 光子学报, 2019, 48(5): 0510001. TIAN Zi-jian, WANG Man-li, WU Jun, GUI Wei-feng, WANG Wen-qing. Mine Image Enhancement Algorithm Based on Dual Domain Decomposition[J]. ACTA PHOTONICA SINICA, 2019, 48(5): 0510001.

基于双域分解的矿井下图像增强算法

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