The physical imaging model, which is based on atmospheric absorption and scattering, plays an important role in single-image dehazing. It is critical that the transmission is accurately estimated for the dehazing algorithm based on the physical imaging model. A self-adaptive weighted least squares (AWLS) model is proposed to refine the rough transmission, which is extracted by the dark channel (DC) model. In our model, the gray-world hypothesis and a smoothing technique with edge preservation are integrated to optimize the transmission and remove the artifacts which are brought by the DC model. The self-AWLS model has higher computational efficiency and can prevent the distortion of the recovered image better when the hazy image contains sky region, while many other dehazing techniques are not applicable for such images. Experimental results show that the proposed model is both effective and efficient for the haze removal application.