Convex Hybrid Restoration and Segmentation Model for Color Images

Image restoration and segmentation are important areas in digital image processing and computer vision. In this paper, a new convex hybrid model is proposed for joint restoration and segmentation during the post-processing of colour images. The proposed Convex Hybrid model is compared with the existing state of the art variational models such as Cai model, Chan-Vese Vector-Valued (CV-VV) model and Local Chan-Vese (LCV) model using noises such as Salt & Pepper and Gaussian. Additional four experiments were performed with increasing combination of noises such as Salt & Pepper, Gaussian, Speckle and Poisson to thoroughly verify the performance of Convex Hybrid Model. The results revealed that the Convex Hybrid model comparatively outperformed qualitatively and has successfully removed the noises and segment the required object properly. The Convex Hybrid model used the colour Tele Vision (TV) as a regularizer for denoising of the corrupt image. The Convex Hybrid Model is convex and can get global minima. The PDEs obtained from the minimisation of the Convex Hybrid Model are numerically solved by using explicit scheme.

@article{Matiullah2018,
  title     = {Convex Hybrid Restoration and Segmentation Model for Color Images},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {4},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Matiullah and Samiullah Khan and Noor Badshah and Fahim ullah and Ziaulla},
  doi       = {10.14569/IJACSA.2018.090449},
  url       = {https://doi.org/10.14569/IJACSA.2018.090449}
}