Football Video Image Restoration Based on Generalized Equalized Fuzzy C-mean Clustering Algorithm

With the development of image processing techniques, the quality of visual content has become crucial for acquiring and analyzing information, especially in applications in the field of sports, such as football match videos. Conventional image restoration techniques have limitations in dealing with motion blur and noise interference, especially in maintaining edge information and texture details. Aiming at these challenges, the study presents a generalized balanced fuzzy C-mean clustering algorithm incorporating fuzzy logic and cluster analysis by introducing local spatial information and adaptive edge protection factors, and the generalized balanced fuzzy C-mean clustering algorithm optimizes the updating strategies of the affiliation function and the class center in order to enhance the detail preservation and noise suppression, aiming to improve the recovery quality of football video images. The results demonstrated that the average gradient ratio, edge strength, standard deviation, and information entropy of the designed algorithm were 1.77, 0.92, 0.26, and 1.73, respectively, which were significantly better than those of other algorithms, proving its superiority in image restoration. Football video images can be made clearer and more detailed with the help of the generalized balanced fuzzy C-mean clustering technique, which also advances motion analysis and automatic identification technologies.

@article{Liu2024,
  title     = {Football Video Image Restoration Based on Generalized Equalized Fuzzy C-mean Clustering Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Shaonan Liu},
  doi       = {10.14569/IJACSA.2024.01506139},
  url       = {https://doi.org/10.14569/IJACSA.2024.01506139}
}