As an atmospheric phenomenon, haze significantly reduces the visibility of outdoor and remote sensing images. imaging have different mechanisms, existing dehazing methods are hard to be applied for both images In this article, efficient method is proposed which can The has advantages based on image enhancement restoration methods. To address problem inaccurate calculation transmittance in metho...

This paper proposes a Dynamic Multi-Attention Dehazing Network (DMADN) for single image dehazing. The proposed network consists of two key components, the Feature Attention (DFA) module, and Adaptive Fusion (AFF) module. DFA module provides pixel-wise weights channel-wise input features, considering that haze distribution is always uneven in degenerated value each channel different. We propose ...

Image-to-image translation based on generative adversarial network (GAN) has achieved state-of-the-art performance in various image restoration applications. Single dehazing is a typical example, which aims to obtain the haze-free of haze one. This paper concentrates challenging task single dehazing. Based atmospheric scattering model, novel model designed directly generate image. The main chal...

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...

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...

Adversary imaging condition such as in a hazy weather is a challenge in the community of image processing. To address the challenge of haze removal, several model-based approaches have been reported recently. Among them, a single image haze removal scheme based on dark channel prior (DCP) was presented in [1] and has been getting popular because of its satisfactory performance for most of cases...