Hazy or foggy weather conditions significantly degrade the visual quality of an image in an outdoor environment. It also changes the color and reduces the contrast of an image. This paper introduces a novel single image dehazing technique to restore a hazy image without considering the physical model of haze formation. In order to find haze-free image, the proposed method does not require the transmission map and its costly refinement process. Since haze effect is dependent on the depth, it severely degrades the visibility of the objects located at a far distance. The objects close to the camera are unaffected. In this paper, we propose a fusion-based haze removal method based on the joint cumulative distribution function (JCDF) that treats faraway haze and nearby haze separately. The output images after the JCDF module, fused in the gradient domain to produce a haze-free image. The proposed method not only significantly enhances visibility but also preserves texture details. The proposed method is experimented and evaluated on a large set of challenging hazy images (large scene depth, night time, dense fog, etc.). Both qualitative and quantitative measures show that the performance of the proposed method is better than the state-of-the-art dehazing techniques.

朦胧或有雾的天气条件会严重降低室外环境中图像的视觉质量。它还会更改颜色并降低图像的对比度。本文介绍了一种新颖的单图像去雾技术，可在不考虑雾度形成的物理模型的情况下恢复雾度图像。为了找到无雾图像，所提出的方法不需要透射图及其昂贵的细化过程。由于雾度取决于深度，因此严重降低了远处物体的可见性。靠近相机的物体不受影响。在本文中，我们提出了基于联合累积分布函数（JCDF）的基于融合的雾度去除方法，该方法分别处理遥远的雾度和附近的雾度。JCDF模块之后的输出图像，在梯度域中融合以产生无雾图像。所提出的方法不仅显着增强了可见性，而且保留了纹理细节。对大量具有挑战性的模糊图像（大景深，夜间，浓雾等）进行了实验和评估。定性和定量方法均表明，该方法的性能优于最新的除雾技术。