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Applications of 3 Threshold Methods to the Lightning Channel Image Recognition
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摘要: 该文实现了3种常见的阈值算法在闪电通道识别中的应用。针对某些闪电通道图像对比度较低的特点，首先对原始图像去除背景光照不均的影响并进行图像增强，再根据所处理的闪电通道图像的具体情况，选择全局自适应阈值法、局部自适应阈值法或基于Otsu自适应阈值的Canny算子对增强后的图像进行边缘检测，进而对闪电通道进行识别。对所提取到的闪电通道进行形态学和细化处理，最终提取到由连续的像素点序列表示的闪电通道。针对数字图像中闪电通道背景的不同特点，文中提到的3种阈值方法能够有针对性地对不同图像进行通道检测，在保证准确获得清晰边缘信息的同时，较好地兼顾了对弱边缘信息的提取，有效保证了对闪电通道的识别效果。Abstract:
General digital cameras, camcorders as well as the BOYS camera specially developed for the lightning observation, are all important tools for lightning research. They are used to obtain the basic data for understanding geometry features of channels and main development processes of lightning. For a long time, extracting lightning channel coordinates from digital images is based on manual processing methods with the relatively low efficiency. With the development of the photoelectric techniques, more and more advanced optical devices are used in lightning observation, such as high-speed cameras and the Automatic Lightning Processing Feature Observation System (ALPS), and data obtained become much richer. How to automatically process these data and improve the efficiency of data extraction and analysis is an urgent need to be addressed.Considering the complexity of lightning discharges and various characteristics of the lightning channel, only one algorithm is not enough to obtain a satisfying recognition result in all situations. Therefore, 3 common threshold methods are applied jointly in the lightning channel recognition. Firstly, the impact of the uneven illumination is eliminated by subtracting the background and the contrast of the image is enhanced. Secondly, global adaptive threshold method, local adaptive threshold method or adaptive Canny operator method is applied for edge detection. And then, morphological and thinning processes are carried out to get the lightning channel represented by the continuous sequence of pixels. Considering different characteristics of the lightning channel digital image, selecting appropriate algorithm can ensure getting a clear edge information even including weak edges, guaranteeing a good recognition effect finally.Through experiments, it can be concluded that for the lightning channel with simple structure and relatively uniform brightness, all algorithms mentioned above can get a good recognition result, among which the global adaptive threshold method is simpler and more efficient. Local adaptive threshold method can calculate the threshold for different images universally. And for the low-contrast images with a smooth background, using adaptive Canny operator method can achieve a satisfactory recognition result.
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图 2 图像预处理结果
(a) 闪电通道原图，(b) 背景图像，(c) 背景差法获得的闪电通道图像，(d) 增强后的闪电通道图像
Fig. 2 Image preprocessing results
(a) the original image of the lightning channel, (b) background image, (c) the lightning channel image obtained by the frame difference method, (d) the enhanced lightning channel image
图 3 3种阈值方法闪电通道识别结果
(a) 自适应阈值的Canny算子，(b) 全局自适应阈值法，(c) 局部自适应阈值法
Fig. 3 Results of the lightning channel recognition by three kinds of threshold methods
(a) adaptive Canny operator method, (b) global adaptive threshold method, (c) local adaptive threshold method
图 6 闪电通道识别结果示例1
(a) 闪电通道原图，(b) 自适应阈值的Canny算子，(c) 全局自适应阈值法，(d) 局部自适应阈值法
Fig. 6 Lightning channel recognition of Example 1
(a) the original image of the lightning channel, (b) adaptive Canny operator method, (c) global adaptive threshold method, (d) local adaptive threshold method
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