Most of the computer applications use digital images. Digital image processing acts an important role in the analysis and interpretation of data, which is in the digital form. Images and videos of outdoor scenes are mainly affected by the bad weather conditions such as haze, fog, mist etc. It will result in poor visibility of the scene caused by the lack of quality. It will make awful impact on...

Single image dehazing is an important low-level vision task with many applications. Early researches have investigated different kinds of visual priors to address this problem. However, they may fail when their assumptions are not valid on specific images. Recent deep networks also achieve relatively good performance in this task. But unfortunately, due to the disappreciation of rich physical r...

Despite the recent progress in image dehazing, several problems remain largely unsolved such as robustness for varying scenes, the visual quality of reconstructed images, and effectiveness and flexibility for applications. To tackle these problems, we propose a new deep network architecture for single image dehazing called DR-Net. Our model consists of three main subnetworks: a transmission pre...

The dark channel prior (DCP) technique is an effective method to enhance hazy images. Dark channel is an image with the same size as the hazy image which represents the haze severity in different places of the image. The DCP method suffers from two problems: it is incapable for removing haze from smooth regions, causing blocking effects on these areas; it cannot properly reduce a haze with a no...

Abstract In recent years, many methods based on deep learning have emerged in the field of image dehazing. The ViTGAN generation adversarial network Transformer is used to design dehazing algorithm.In ViTGAN, generator improved achieve desired effect.The first half collaterals can be as a data encoder extract features original image. this part, lower sampling layer placed behind each density bl...