A Fast Single-Image Dehazing Algorithm Based on Dark Channel Prior and Rayleigh Scattering

The scattering of atmospheric particles significantly alters images captured under hazy weather condition. Images appear distorted, blurry and low in contrast attenuation, which extensively affects computer vision systems. There has been development of several prior based methods to address this problem. However, these methods come at a high computational cost. We present a fast, single image dehazing method based on dark channel prior and Rayleigh scattering. Firstly, we present a simple but effective methodology for estimating the atmospheric light through the computation of average, minimum and maximum of the pixels in each of the three RGB colour channels. Then, using the theory of Rayleigh scattering, we model a scattering coefficient to estimate the initial transmission map. Also, a fast-guided filter is adopted to refine the initial transmission map due to inaccurate halo edges. Finally, we restore the haze-free image through the atmospheric scattering model. Extensive qualitative and computational experiments on hazy outdoor images demonstrate that the proposed method produces excellent results whiles achieving a faster processing time.