Image de-hazing based on optimal compression and histogram specification

Haze in the environment will hinder the accurate recognition of objects captured in an image. To overcome this problem, image de-hazing processes have been an active technique applied in many research work. Among the available approaches, the one based on the assumption of dark channel prior is able to produce promising results and improved processing speed by integrating the guided filter. However, there are still some limitations existing in this method; particularly the over-range problem makes the appearance of recovered image unnatural. Moreover, its incapability in preserving image brightness frequently requires user intervention. In order to alleviate these shortcomings, the approach presented in this paper is realized through an effective magnitude compression operation. Histogram specification is further exercised for image post-processing. Finally, parameters of both steps are optimized with the particle swarm optimization algorithm. Experiments were conducted with one hundred and thirty hazy images captured in different environmental conditions. Results showed that the proposed method performs better or equivalently in image de-hazing comparing with the approach based on dark channel prior.