Regional Atmospheric Light Optimisation Algorithm for Heterogeneous Image Dehazing

Abstract

Under foggy and other severe weather conditions, image acquisition equipment is not effective. It often produces an image with low contrast and low scene brightness, which is difficult to use in other image-based applications. The dark channel prior dehazing algorithm will cause the brightness of the image to decrease and sometimes introduce halos in the sky area. To solve this problem, we proposed a region similarity optimisation algorithm based on a dark channel prior. First, a vector comprising RGB layer dark channel value was obtained as the original atmospheric ambient light, and then, the proposed regional similarity linear function was used to adjust the atmospheric ambient light matrix. Next, the transmittance of different colour channels was derived and the multichannel soft matting algorithm was employed to produce more effective transmittance. Finally, the atmospheric ambient light and transmittance were substituted into the atmospheric scattering model to calculate clean images. Experimental results show that the proposed algorithm outperformed the existing mainstream dehazing algorithms in terms of both visual judgement and quality analysis with nonhomogeneous haze datasets. The algorithm not only improves the image details but also improves the brightness and saturation of the dehazing result; therefore, the proposed algorithm is effective in the restoration of the hazy image.