A Single Image Dehazing Method Using Average Saturation Prior

Abstract

Outdoor images captured in bad weather are prone to yield poor visibility, which is a fatal problem for most computer vision applications. The majority of existing dehazing methods rely on an atmospheric scattering model and therefore share a common limitation; that is, the model is only valid when the atmosphere is homogeneous. In this paper, we propose an improved atmospheric scattering model to overcome this inherent limitation. By adopting the proposed model, a corresponding dehazing method is also presented. In this method, we first create a haze density distribution map of a hazy image, which enables us to segment the hazy image into scenes according to the haze density similarity. Then, in order to improve the atmospheric light estimation accuracy, we define an effective weight assignment function to locate a candidate scene based on the scene segmentation results and therefore avoid most potential errors. Next, we propose a simple but powerful prior named the average saturation prior (ASP), which is a statistic of extensive high-definition outdoor images. Using this prior combined with the improved atmospheric scattering model, we can directly estimate the scene atmospheric scattering coefficient and restore the scene albedo. The experimental results verify that our model is physically valid, and the proposed method outperforms several state-of-the-art single image dehazing methods in terms of both robustness and effectiveness.

Foggy Day Image Sharpening Algorithm Based on Depth of Field Estimation

“…Under normal circu mstances, the larger the proportion of the atmospheric light in the imaging, the higher brightness and blurred textures there is in the area [9][10]. Because of the brightness value of each pixel in the foggy image, it increases as the fog concentration increases.…”

An Effective and Robust Single Image Dehazing Method Using the Dark Channel Prior

“…However, the accuracy of these methods is highly dependent on the validity of the priors and, therefore, may fail in some specific cases for which the assumption is broken. On the basis of the detected sky region ∆ sky or the candidate sky region∆, we can significantly improve the accuracy of global atmospheric light location since most white or highlight objects will be effectively excluded [20].…”

“…As demonstrated in Figure 6, only our method (in the red box) locates the atmospheric light successfully, while Namer et al's method [33] (in the green box), He et al's method [7] (in the blue box) and Zhu et al's method [6] (in the yellow box) all locate the interference object as the atmospheric light. On the basis of the detected sky region sky Δ or the candidate sky region  Δ , we can significantly improve the accuracy of global atmospheric light location since most white or highlight objects will be effectively excluded [20].…”

A Low-Light Image Enhancement Method Based on Image Degradation Model and Pure Pixel Ratio Prior

“…Nevertheless, this prior is not robust for the dark scenes because the scene information is obviously insufficient. Gu et al [41] proposed the average saturation prior which indicates the average saturation for a high-definition clearday outdoor image tends to be around a specific value with a high probability; however, the transmission map can be only obtained when the image depth structure is known.…”