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Haze removal has become an attractive topic in recent years and several dehazing methods are proposed. Dark channel prior (DCP) is one of the most effective dehazing approaches. However, when dealing with images containing large white objects, DCP often mistakes white objects for opaque haze. It will cause the airlight to be overestimated and the transmission to be underestimated, and thus the dehazing results have serious color distortion. In view of the above problem, saliency detection is introduced into haze removal to obtain better restored images in this paper. We first propose a method for reliable airlight estimation. Then, a saliency prior is presented for hazy images, which can distinguish white objects from dense haze by saliency detection. On the basis of saliency prior, both accurate airlight and a correct transmission map can be obtained from images containing large white objects, and finally these images can be restored successfully. The experimental results illustrate that our proposed method has great superiority in color recovery compared with other state-of-art methods when dealing with images containing large white objects. ©20i8SPiEandIS&T[DDI: 10.1117/1. JEI.27.2.023038] [ABSTRACT FROM AUTHOR]

Haze removal is a nontrivial work for medium-altitude unmanned aerial vehicle (UAV) image processing because of the effects of light absorption and scattering. The challenges are attributed mainly to image distortion and detail blur during the long-distance and large-scale imaging process. In our work, a metadataassisted nonuniform atmospheric scattering model is proposed to deal with the aforementioned problems of medium-altitude UAV. First, to better describe the real atmosphere, we propose a nonuniform atmospheric scattering model according to the aerosol distribution, which directly benefits the image distortion correction. Second, considering the characteristics of long-distance imaging, we calculate the depth map, which is an essential clue to modeling, on the basis of UAV metadata information. An accurate depth map reduces the color distortion compared with the depth of field obtained by other existing methods based on priors or assumptions. Furthermore, we use an adaptive median filter to address the problem of fuzzy details caused by the global airlight value. Experimental results on both real flight and synthetic images demonstrate that our proposed method outperforms four other existing haze removal methods. [ABSTRACT FROM AUTHOR]

To prevent halo artifacts resulting from edge preserving smoothing methods that use a local filter, a filter method with a guided image that fuses multiple kernels is proposed. This method first computes the coefficients of different local multiple kernels at the pixel level and then linearly fuses these coefficients to obtain the final coefficients. Finally, the filtered image is generated using the linear coefficients. Compared with existing methods, including the popular bilateral filter and guided filter methods, our experimental results show that the proposed method not only obtains images with better visual quality but also prevents halo artifacts, resulting in detail enhancement, haze removal, and noise reduction. [ABSTRACT FROM AUTHOR]

We present an image fusion algorithm for a visible image and a near-infrared image. The proposed algorithm synthesizes a fused image that includes high-visibility information of both images while reducing artifacts caused by geometric and illumination inconsistencies. In the proposed fusion, the high-visibility area is labeled at each pixel by global optimization based on the local visibility and inconsistency. The local visibility is evaluated using a local contrast. The inconsistency is also locally estimated based on a learning-based approach. The fused luminance is constructed using Poisson image reconstruction that preserves the gradient of the selected high-visibility areas. The proposed fusion framework has various applications, which include denoising, haze removal, and image enhancement. Experimental results show that the proposed method has comparable or even superior performance to existing methods designed for specific applications. [ABSTRACT FROM AUTHOR]

The attenuation of the light transmitted through air can reduce image quality when taking a photograph outdoors, especially in a hazy environment. Hazy images often lack sufficient information for image recognition systems to operate effectively. In order to solve the aforementioned problems, this study proposes a hybrid method combining fuzzy theory with weighted estimation for the removal of haze from images. A transmission map is first created based on fuzzy theory. According to the transmission map, the proposed method automatically finds the possible atmospheric lights and refines the atmospheric lights by mixing these candidates. Weighted estimation is then employed to generate a refined transmission map, which removes the halo artifact from around the sharp edges. Experimental results demonstrate the superiority of the proposed method over existing methods with regard to contrast, color depth, and the elimination of halo artifacts. [ABSTRACT FROM AUTHOR]