A Fish Retina-Inspired Single Image Dehazing Method

Abstract

Outdoor images are significantly degraded by bad weather conditions, such as fog and dust, which seriously limits efficient information extraction by computer vision applications. Most existing weather-degraded hazy image enhancement methods ignore the wavelength dependence of the scattering coefficient and therefore cannot well handle the colorized haze in which the medium transmission varies in different color channels. In this work, we propose a fish retina-inspired method to solve this problem. Fish have special retinal mechanisms for extracting useful information from underwater environments where photons are scattered and absorbed spatially introducing wavelength-dependent degradation of visibility. Due to the similarity between suspensions in water and aerosols in the air, these mechanisms should also be suitable for processing hazy scenes. The proposed method imitates bipolar cells with scene-dependent center-surround receptive fields to eliminate redundant information, ganglion cells with nonlinear processing to boost contrast, and the centrifugal pathway from amacrine cells to the horizontal cells to adaptively protect visual information from excessive lateral suppression. Compared with state-of-the-art methods on various images degraded by fog, haze, or dust, the proposed method shows quite competitive performance both qualitatively and quantitatively.