1
MS.c Student of Artificial intelligence, Faculty of Computer Engineering, Shahrood University OF Technology
2
Faculty of Computer Eng., Shahrood University of Technology
Abstract
Blurriness is one of the common distortions in images. This distortion is caused by spilling the pixel information overthe adjacent pixels. Blurriness has different types. The knowledge about the type of image blurriness is one of the important parameters which directly affects performance of de-blurring methods.In this paper, a method has been proposed to classify the fourtypes of global blurrinessin digital imagesin the spatial domain. These blurriness include the Gaussian blur, rectangular blur, motion blur and defocus blur. In the proposed method, the correlation concept is used to classify the type of image blurriness. The correlation concept depicts the relations between the image pixels. Also, the model and correlation of adjacent pixels are proportional to the type of blurriness.Appropriate features are extracted to detect the type of blurriness. The accuracy of the proposed method for detecting the type of blurriness is 90.4%. This method has a better performance compared to other existing methods in terms of accuracy and computational cost.
Alipour, E., Hassanpour, H., & fateh, M. (2020). Image Blurriness Classification in Global Blur. Journal of Machine Vision and Image Processing, 6(2), 105-118.
MLA
Elahe Alipour; Hamid Hassanpour; mansoor fateh. "Image Blurriness Classification in Global Blur". Journal of Machine Vision and Image Processing, 6, 2, 2020, 105-118.
HARVARD
Alipour, E., Hassanpour, H., fateh, M. (2020). 'Image Blurriness Classification in Global Blur', Journal of Machine Vision and Image Processing, 6(2), pp. 105-118.
VANCOUVER
Alipour, E., Hassanpour, H., fateh, M. Image Blurriness Classification in Global Blur. Journal of Machine Vision and Image Processing, 2020; 6(2): 105-118.
)
