4.6 Article

A Fast Defogging Image Recognition Algorithm Based on Bilateral Hybrid Filtering

出版社

ASSOC COMPUTING MACHINERY
DOI: 10.1145/3391297

关键词

IoT; defogging image; bilateral hybrid filtering; robustness

资金

  1. National Natural Science Foundation of China [61872138, 62072170]
  2. Hunan Provincial Science & Technology Project Foundation [2018TP1018, 2020JJ5369]
  3. Scientifc Research Fund of the Hunan Provincial Education Department [19C1157]
  4. Start-Up Funds of Hunan Normal University [531120-3812]
  5. Fujian Provincial Natural Science Foundation of China [2018J01570]
  6. Guangxi Key Laboratory of Crytography and Information Security [GCIS201920]
  7. Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) [MJUKF-IPIC202008]

向作者/读者索取更多资源

This paper proposes a fast defogging image recognition algorithm based on bilateral hybrid filtering. Experimental results show promising defogging effect and speed, with the image recognition rate reaching 98.8% after defogging.
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.

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