Underwater Video Image Restoration and Visual Communication Optimization Based on Improved Non Local Prior Algorithm

Underwater image processing should balance image clarity restoration and comprehensive display of underwater scenes, requiring image fusion and stitching techniques. The pixel level fusion method is based on pixels, and by fusing different image data, it eliminates stitching gaps and sudden changes in lighting intensity, preserves detailed information, and thus improves the accuracy of stitching images. In the process of restoring underwater video images without local priors, there is still room for optimization in steps such as removing atmospheric light values, estimating transmittance, and calculating dehazing images through regularization. Based on the characteristics of Jerlov water types, water quality is classified according to the properties of suspended solids, and each channel is adjusted to the compensation space to improve the restoration algorithm. Background light estimation is used to determine the degree of image degradation, select the optimal attenuation coefficient ratio, and restore the image. The experimental results show that it is crucial to choose a ratio of attenuation coefficients that is close to the actual water quality environment being photographed. Both this model and traditional algorithms have an accuracy rate of over 99.0%, with the accuracy of this model sometimes reaching 99.9%. Pixel level fusion and background light estimation technology optimize underwater images, improve stitching accuracy and clarity, enhance target detection and recognition, and have important value for marine exploration rigs.

@article{Xia2024,
  title     = {Underwater Video Image Restoration and Visual Communication Optimization Based on Improved Non Local Prior Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Tian Xia},
  doi       = {10.14569/IJACSA.2024.0150422},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150422}
}