Local Binary Pattern Based Defocus Blur Detection Using Adaptive Threshold

  • Mahmood, Muhammad Tariq (School of Computer Science and Engineering, Korea University of Technology and Education) ;
  • Choi, Young Kyu (School of Computer Science and Engineering, Korea University of Technology and Education)
  • Received : 2020.07.07
  • Accepted : 2020.08.18
  • Published : 2020.09.30


Enormous methods have been proposed for the detection and segmentation of blur and non-blur regions of the images. Due to the limited available information about the blur type, scenario and the level of blurriness, detection and segmentation is a challenging task. Hence, the performance of the blur measure operators is an essential factor and needs improvement to attain perfection. In this paper, we propose an effective blur measure based on the local binary pattern (LBP) with the adaptive threshold for blur detection. The sharpness metric developed based on LBP uses a fixed threshold irrespective of the blur type and level which may not be suitable for images with large variations in imaging conditions and blur type and level. Contradictory, the proposed measure uses an adaptive threshold for each image based on the image and the blur properties to generate an improved sharpness metric. The adaptive threshold is computed based on the model learned through the support vector machine (SVM). The performance of the proposed method is evaluated using a well-known dataset and compared with five state-of-the-art methods. The comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all the methods.


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