Enhancement of image degraded by fog using cost function based on human visual model

In foggy weather conditions, images become degraded due to the presence of airlight that is generated by scattering light by fog particles. In this paper, we propose an effective method to correct the degraded image by subtracting the estimated airlight map from the degraded image. The airlight map is generated using multiple linear regression, which models the relationship between regional airlight and the coordinates of the image pixels. Airlight can then be estimated using a cost function that is based on the human visual model, wherein a human is more insensitive to variations of the luminance in bright regions than in dark regions. For this objective, the luminance image is employed for airlight estimation. The luminance image is generated by an appropriate fusion of the R, G, and B components. Representative experiments on real foggy images confirm significant enhancement in image quality over the degraded image.