LAUSR

Remote sensing images taken during poor environmental conditions are degraded by the scattering of atmospheric particles, which affects the performance of many imaging systems. Hence, an efficient visibility restoration model is required to remove haze from distorted images. But, the design of visibility restoration models is an ill-posed problem as the physical information like depth information and attenuation model are usually unknown. The physical parameters computed using existing models such as dark channel prior and gradient channel prior are not accurate especially for images with large haze gradients. Therefore, in this paper, an evolving visibility restoration model is proposed for remote sensing images. Initially, the fusion-based transmission map is computed from the foreground and sky regions. The transmission map is further improved by designing a hybrid constraints-based variational model. Finally, a dynamic differential evolution is utilized to optimize the control parameters of the proposed model. The proposed model is validated on fifty synthetic benchmarks and fifty real-life remote sensing images. For comparative analysis, ten well-known restoration models are also considered. The comparative analysis demonstrates that the proposed model outperforms the existing restoration models.