Foggy days limit the functionality of outdoor surveillance systems. However, it is still a challenge for existing methods to maintain the uniformity of defogging between image regions with a similar depth of field and large differences in appearance. To address above problem, this article proposes a regional gradient constrained prior (RGCP) for defogging that uses the piecewise smoothing characteristic of the scene structure to achieve accurate estimation and reliable constraint of the transmission. RGCP first derives that when adjacent similar pixels in the fog image are aggregated and spatially divided into regions, clusters of region pixels in RGB space conform to a chi-square distribution. The offset of the confidence boundary of the clusters can be regarded as the initial transmission of each region. RGCP further uses a gradient distribution to distinguish different regional appearances and formulate an interregional constraint function to constrain the overestimation of the transmission in the flat region, thereby maintaining the consistency between the estimated transmission map and the depth map. The experimental results demonstrate that the proposed method can achieve natural defogging performance in terms of various foggy conditions.

Color constancy is the ability to remove the effect of illumination on color. Since color constancy is an ill-posed problem, many methods have been proposed based on assumptions to constraint the solution space. However, most existing assumptions require specular pixels or abundant colors, and fail to produce satisfactory results for different scenarios. According to extensive experiments, we observe that the chromaticity distribution of pixels within main color under canonical illumination, which we called canonical pixels, is linear and can also locate the position of illumination under the non-canonical illumination. Therefore, this paper proposes a chromaticity-line prior (CLP) as an additional linear constraint on the ill-posed problem of color constancy. In the calculation of CLP, the simple linear iterative clustering is firstly employed to segment an image into several super-pixel blocks. And the random sampling consensus is utilized to remove non-primary color points and fit the chromaticity-line. Based on the proposed CLP, a color constancy algorithm is implemented correspondingly. Since the main idea of the CLP is to extract the canonical pixels, which is the inherent property of image, the proposed CLP is more general and adaptive in real scenes. The experiments on two public datasets demonstrate that the proposed algorithm not only outperforms state-of-the-art learning-free algorithms, but also achieves results that are competitive to those of learning-based algorithms.

Remote sensing images play important roles in various earth surface observation applications. However, the hazy state of surface atmosphere can visually decrease the contrast and availability of remote sensing images. In this paper, we propose a haze and thin cloud removal method for single visible remote sensing images, which aims to robustly estimate haze thickness, atmospheric light, and transmission value from a remote sensing image with dense haze or thin cloud, and finally recovers a haze-free image. An elliptical boundary prior (EBP) is proposed to transform the haze thickness in each local patch from the pixels cluster in the spectral space, which is surrounded by an ellipse. With the aim of preventing highlight objects influences, an atmospheric light estimation approach is presented. The correlation of transmission and haze thickness is reconstructed to develop the scattering model for remote sensing images. The experimental results demonstrate that the proposed method can not only significantly improve the contrast and restore textures of various kinds of hazy remote sensing images but also well preserve the spectral information of visible bands.

Single image fog removal is important for surveillance applications and many defogging methods have been proposed, recently. Due to the adverse atmospheric conditions, the scattering properties of foggy images depend on not only the depth information of scene, but also the atmospheric aerosol model, which has more prominent influence on illumination in a fog scene than that in a haze scene. However, recent defogging methods confuse haze and fog, and they fail to consider fully about the scattering properties. Thus, these methods are not sufficient to remove fog effects, especially for images in maritime surveillance. Therefore, this paper proposes a single image defogging method for visual maritime surveillance. Firstly, a comprehensive scattering model is proposed to formulate a fog image in the glow-shaped environmental illumination. Then, an illumination decomposition algorithm is proposed to eliminate the glow effect on the airlight radiance and recover a fog layer, in which objects at the infinite distance have uniform luminance. Secondly, a transmission-map estimation based on the non-local haze-lines prior is utilized to constrain the transmission map into a reasonable range for the input fog image. Finally, the proposed illumination compensation algorithm enables the defogging image to preserve the natural illumination information of the input image. In addition, a fog image dataset is established for visual maritime surveillance. The experimental results based on the established dataset demonstrate that the proposed method can outperform the state-of-the-art methods in terms of both the subjective and objective evaluation criteria. Moreover, the proposed method can effectively remove fog and maintain naturalness for fog images.

Single-image haze removal is important for many practical applications (e.g., surveillance). However, dehazed results of existing algorithms tend to be oversmoothed with missing fine image details. This drawback is caused by two factors: inaccurate airlight estimations and disregarding multiple scattering. In this paper, we propose a detail-preserving image dehazing algorithm based on two key priors, namely, the depth-edge aware prior and the airlight impact regularity prior. The proposed algorithm makes contributions in both the haze removal step and the postprocessing step. First, based on the depth-edge aware prior, an airlight refinement algorithm is proposed. The gradient strength of the minimum channel is employed to calculate punishment weights to smooth the dark channel. Second, based on the airlight impact regularity prior, an adaptive sharpening model that considers the refined airlight to determine the sharpening strength value is established to enhance levels of detail. Experimental results demonstrate that the proposed algorithm cannot only effectively remove haze but can also enhance levels of detail to thus outperform the state of the art on a wide variety of images.

Illumination estimation is important for image enhancement based on Retinex. However since illumination estimation is an ill-posed problem it is difficult to achieve accurate illumination estimation for nonuniform illumination images. The conventional illumination estimation algorithms fail to comprehensively take all the constraints into the consideration such as spatial smoothness sharp edges on illumination boundaries and limited range of illumination. Thus these algorithms cannot effectively and efficiently estimate illumination while preserving naturalness. In this paper we present a naturalness preserved illumination estimation algorithm based on the proposed joint edge-preserving filter which exploits all the abovementioned constraints. Moreover a fast estimation is implemented based on the box filter. Experimental results demonstrate that the proposed algorithm can achieve the adaptive smoothness of illumination beyond edges and ensure the range of the estimated illumination. When compared with other state-of-the-art algorithms it can achieve better quality from both subjective and objective aspects.

Visible videos captured under different weather conditions may exhibit different characteristics, and thermal infrared videos are easily affected by ambient temperature variations; this sensitivity to environmental conditions makes the fusion of visible and thermal infrared videos a challenge. This paper proposes an adaptive fusion algorithm for visible and infrared videos, and uses cumulative distribution of gray levels and the entropy to adaptively retain infrared-hot targets and visible textures. The original visible and infrared frames are decomposed into two layers, namely, the base layer and the detail layer. The guided filter is employed to decompose frames due to its high efficiency. Two weight maps, one for the infrared base layer and one for the visible base layer, are adaptively generated based on the cumulative distribution of gray levels and the entropy, respectively. The visible base layer and the infrared base layer are fused based on their weight maps. The final fusion result is obtained by combining the fused base layer with the visible detail layer. Experimental results demonstrate that the proposed algorithm can achieve better fusion results compared with state-of-the-art methods.