A new filtering algorithm for video monitoring image of coal mine
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摘要: 针对煤矿视频监控图像存在噪声强度高且对比度低等问题,提出了一种新型煤矿视频监控图像滤波算法。该算法首先采用自适应Canny算子对图像进行边缘检测,实现边缘图像和非边缘图像的有效分离;然后对边缘图像引入直方图均衡化算法进行处理,以突出图像边缘信息,提高图像对比度;从滤波器的构建、结构元素的设计方面对经典数学形态学滤波算法进行改进,将其应用于非边缘图像的滤波;最后对处理后的边缘图像和非边缘图像引入图像融合机制进行加权融合。实验结果表明,与小波阈值法、经典数学形态学滤波算法相比,该算法具有较好的滤波效果。Abstract: In view of problems of high noise intensity and low contrast of video monitoring image of coal mine, a new filtering algorithm for video monitoring image of coal mine was proposed. Firstly, edge of image is detected by use of self-adaptive Canny operator, so as to realize effective separation of edge image and non-edge image. Then histogram equalization algorithm is introduced to process the edge image, so as to highlight edge information and improve contrast of the image. Meanwhile, classical mathematical morphology filtering algorithm is improved through construction of filter and design of structural element, and it is applied to filtering of the non-edge image. Finally, image fusion mechanism is introduced to realize weight fusion of the processed edge image and non-edge image. The experimental results show that the algorithm has better filtering effect than wavelet threshold algorithm and classical mathematical morphology filtering algorithm.
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