전기전자학회논문지 (Journal of IKEEE)

  • 제22권4호
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  • Pages.948-952
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  • 2018
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  • 1226-7244(pISSN)
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  • 2288-243X(eISSN)
한국전기전자학회 (Institute of Korean Electrical and Electronics Engineers)
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Improving Performance of Machine Learning-based Haze Removal Algorithms with Enhanced Training Database

  • Ngo, Dat (Dept. of Electronics Engineering, Dong-A University) ;
  • Kang, Bongsoon (Dept. of Electronics Engineering, Dong-A University)
  • 투고 : 2018.11.23
  • 심사 : 2018.12.21
  • 발행 : 2018.12.31
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초록

Haze removal is an object of scientific desire due to its various practical applications. Existing algorithms are founded upon histogram equalization, contrast maximization, or the growing trend of applying machine learning in image processing. Since machine learning-based algorithms solve problems based on the data, they usually perform better than those based on traditional image processing/computer vision techniques. However, to achieve such a high performance, one of the requisites is a large and reliable training database, which seems to be unattainable owing to the complexity of real hazy and haze-free images acquisition. As a result, researchers are currently using the synthetic database, obtained by introducing the synthetic haze drawn from the standard uniform distribution into the clear images. In this paper, we propose the enhanced equidistribution, improving upon our previous study on equidistribution, and use it to make a new database for training machine learning-based haze removal algorithms. A large number of experiments verify the effectiveness of our proposed methodology.

키워드

JGGJB@_2018_v22n4_948_f0001.png 이미지

Fig. 1. Training procedure proposed by Zhu et al. consists of training data preparation (upper part) and supervised learning process (lower part)

JGGJB@_2018_v22n4_948_f0002.png 이미지

Fig. 2. Histogram of 1,000 random numbers drawn from SUD on the open interval (0, 1)

JGGJB@_2018_v22n4_948_f0003.png 이미지

Fig. 3. An example of applying the enhanced equidistribution to a small random sequence

Table 1. Average e, r values on IVC

JGGJB@_2018_v22n4_948_t0001.png 이미지

Table 2. Average TMQI, MSE values on FRIDA2

JGGJB@_2018_v22n4_948_t0002.png 이미지

참고문헌

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