Image fusion approach with noise reduction using Genetic algorithm

Image fusion is becoming a challenging field as for its importance to different applications, Multi focus image fusion is a type of image fusion that is used in medical fields, surveillances, and military issues to get the image all in focus from multi images every one is in focus in a different part, and for making the input images more accurate before making the fusing process we use Genetic Algorithm (GA) for image de-noising as a preprocessing process. In our research paper we introduce a new approach that begin with image de-noising using GA and then apply the curvelet transform for image decomposition to get a multi focus image fusion image that is focused in all of its parts. The results show that Curvelet transform had been proven to be effective at detecting image activity along curves, and increasing the quality of the obtained fused images. And applying the mean fusion rule for fusing multi-focus images gives accurate results than PCA, contrast and mode fusion rule, Also, GA shows more accurate results in image de-noising after comparing it to contourlet transform.

@article{Taher2013,
  title     = {Image fusion approach with noise reduction using Genetic algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {4},
  number    = {11},
  year      = {2013},
  publisher = {The Science and Information Organization},
  author    = {Gehad Mohamed Taher and Mohamed Elsayed Wahed and Ghada El Taweal and Ahmed Fouad},
  doi       = {10.14569/IJACSA.2013.041103},
  url       = {https://doi.org/10.14569/IJACSA.2013.041103}
}