Image dehazing is inherently an ill-posed problem that involves the extraction of interesting targets from a static image or a video sequence. Such technology is expected to attract wide application in high-level image processing and visual engineering. Transmission estimation is the primary task in single image haze removal. The inhomogeneous fog distribution in a degraded image can lead to false estimations in the transmission estimation process. This paper proposes an image dehazing method that uses prior visual information loss. Given that a hazy image in a natural scene generally exhibits low contrast and chromatic distortion

we ignore the transmission estimation and instead solve the optimization problems of the information loss function. First

the proposed method divides hazing images into three vision areas according to fog density. Second

the loss function

which is built based on the visible characteristics of hazy images

solves the local minimum transmission via the stochastic gradient descent method. Third

the divided dehazed areas are fused via multi-scale illuminance image segmentation with a linear filter. Fourth

the scene albedo is recovered by employing an atmospheric scattering model that uses global transmission. The proposed and existing dehazing methods are qualitatively and quantitatively evaluated to assess their image dehazing performance. The experimental results show that the proposed algorithm effectively removes haze from the degraded image and achieves higher-quality

halo-free

and detailed restorations than the existing dehazing methods. On the average

the proposed method achieves 20% higher-quality restorations than the classic haze removal algorithms. The proposed method significantly enhances image visibility and demonstrates better image haze removal performance than the existing dehazing methods. Compared with the state-of-the-art method

the proposed algorithm is more successful in recovering images from moderate to thick foggy areas and is faster in real-time dehazing applications. The multi-scale and patch-based structure of our method allows us to reduce the running time in neighborhood operations. Future studies can use this method to improve prior knowledge on the effective evaluation mechanism of image dehazing.