Mine image enhancement algorithm based on bilateral filtering and gamma correction

Xiucai Guo, Xiaolei Wei
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Abstract

Aiming at the problems of uneven illumination, detail loss and color distortion of video images caused by low illumination and contrast of mine environment, a mine image enhancement algorithm based on bilateral filtering function is proposed. The algorithm first converts the image from RGB space to HSV space to avoid destroying the color space, extracts the illuminance layer and reflection layer of the image according to the classical Retinex theory combined with bilateral filtering with edge retention, adaptively adjusts the lighting by gamma function, and uses the CLAHE algorithm to enhance the overall contrast. Experimental results show that the proposed algorithm is superior to other commonly used algorithms in terms of visual effects, information entropy, average gradient, standard deviation, etc., which effectively improves the overall brightness and contrast of the image and improves the problems of detail loss and color distortion and realizes the effective enhancement of mine images.
基于双边滤波和伽玛校正的矿山图像增强算法
针对矿山环境低照度和对比度导致的视频图像光照不均匀、细节丢失和色彩失真等问题,提出了一种基于双边滤波函数的矿山图像增强算法。该算法首先将图像从RGB空间转换为HSV空间,避免破坏色彩空间,根据经典的Retinex理论结合边缘保留的双边滤波提取图像的照度层和反射层,通过伽马函数自适应调节光照,并使用CLAHE算法增强整体对比度。实验结果表明,该算法在视觉效果、信息熵、平均梯度、标准差等方面均优于其他常用算法,有效提高了图像的整体亮度和对比度,改善了细节丢失和色彩失真问题,实现了矿山图像的有效增强。
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