Haze removal is an extremely challenging task, and object detection in the hazy environment has recently gained much attention due to popularity of autonomous driving traffic surveillance. In this work, authors propose a multiple linear regression haze model based on widely adopted dehazing algorithm named Dark Channel Prior. Training with synthetic dataset, proposed can reduce unanticipated de...

To train and evaluate fog/haze removal models, it is highly desired but burdensome to collect a large-scale dataset comprising well-aligned foggy/hazy images with their fog-free/haze-free versions. In this paper, we propose a framework, namely Foggy and Hazy Images Simulator (FoHIS for short), to simulate more realistic fog and haze effects at any elevation in images. What’s more, no former stu...

In order to improve the traffic visibility in haze weathers, a Retinex algorithm based on the changing scale for haze removal with a depth map is proposed. It requires the haze image dark channel prior treatment to obtain the estimated depth map. Then it is according to the depth map to calculate Retinex scales for different parts of a hazy image. Finally a single scale Retinex transform is per...

In this paper, we propose a novel method of object detection in bad weather conditions like Fog and smoke. It is based on statistical model of dark channel prior to estimate the thickness of the haze and physics based image restoration approach. Further applying simple image enhancement techniques improve the visibility of an obstacle in the line of sight. Using this prior with this imaging mod...

Clear and wide-angle images are essential to detect and trace objects for applications such as CCTV and unmanned vehicles. Fog, haze, and rain causes problem in getting clear image and lowers the detection rate of objects. To remove haze, we use median dark channel prior which can reduce the complexity of hardware. Image stitching stitches a number of images from multiple cameras into single wi...

We present an approach to easily remove the effects of haze from images. It is based on the fact that usually airlight scattered by atmospheric particles is partially polarized. Polarization filtering alone cannot remove the haze effects, except in restricted situations. Our method, however, works under a wide range of atmospheric and viewing conditions. We analyze the image formation process, ...

The presence of haze significantly degrades the quality remote sensing images, resulting in issues such as color distortion, reduced contrast, loss texture, and blurred image edges, which can ultimately lead to failure application systems. In this paper, we propose a superpixel-based visible dehazing algorithm, namely SRD. To begin, images are divided into content-aware patches using superpixel...