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A Fast Defogging Image Recognition Algorithm Based on Bilateral Hybrid Filtering

机译:一种基于双边混合滤波的快速缺点图像识别算法

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摘要

With the rapid advancement of video and image processing technologies in the Internet of Things, it is urgent to address the issues in real-time performance, clarity, and reliability of image recognition technology for a monitoring system in foggy weather conditions. In this work, a fast defogging image recognition algorithm is proposed based on bilateral hybrid filtering. First, the mathematical model based on bilateral hybrid filtering is established. The dark channel is used for filtering and denoising the defogging image. Next, a bilateral hybrid filtering method is proposed by using a combination of guided filtering and median filtering, as it can effectively improve the robustness and transmittance of defogging images. On this basis, the proposed algorithm dramatically decreases the computation complexity of defogging image recognition and reduces the image execution time. Experimental results show that the defogging effect and speed are promising, with the image recognition rate reaching to 98.8% after defogging.
机译:随着视频和图像处理技术的快速进步,迫切需要解决图像识别技术的实时性能,清晰度和可靠性,在有雾的天气条件下的监测系统。在这项工作中,基于双边混合滤波提出了一种快速的脱模图像识别算法。首先,建立了基于双边混合滤波的数学模型。暗通道用于过滤和去噪该模糊图像。接下来,通过使用引导滤波和中值滤波的组合提出了双边混合滤波方法,因为它可以有效地改善违规图像的鲁棒性和透射率。在此基础上,所提出的算法显着降低了Defogging图像识别的计算复杂度并减少了图像执行时间。实验结果表明,缺点效果和速度很有前景,在缺失后,图像识别率达到98.8%。

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