Image Dehazing Algorithm Using Block Optimization for Transmissivity and Scene Brightness

In the image dehazing process, the estimation of the atmospheric light transmittance is inaccurate, which reduces the scene brightness and causes halo effects in the sky of the dehazed images. To this end, this paper proposes an image de hazing algorithm based on block optimization transmissivity and adaptive scene brightness optimization. This algorithm performs block optimization on transmissivity based on the foggy degree of the image, uses the intensity of airlight to solve the atmospheric scattering model to generate a dehazed image, and then adaptively performs partial adjustment to the gray value of the image to increase scene brightness. Experimental results show that compared with the guided image filtering algorithm and contrast enhancement algorithm, the image dehazed by the proposed algorithm is clearer. The edge-preserving effect is obvious, and the visual effect is better, which means the proposed algorithm is suitable for application fields such as traffic supervision, security monitoring and target recognition.