Considering the frequent occurrence of hazy weather, single image dehazing has become an important research task. Physical model-based and deep learning-based methods are two competitive in dehazing, but achieving fidelity effectively removing haze at same time real scenes is still a challenging problem. In this work, we propose mixed iterative model to restore high-quality clear images by inte...

Haze reduces the visibility of image content and leads to failure in handling subsequent computer vision tasks. In this paper, we address problem single dehazing by proposing a network named T-Net, which consists backbone based on U-Net architecture dual attention module. Multi-scale feature fusion can be achieved using skip connections with new strategy. Furthermore, repeatedly unfolding plain...

In this paper, we propose an end-to-end cross-layer gated attention network (CLGA-Net) to directly restore fog-free images. Compared with the previous dehazing network, model presented in paper uses smooth cavity convolution and local residual module as feature extractor, combined channel mechanism, better extract restored features. A large amount of experimental data proves that defogging prop...

Haze reduces the contrast of an image and causes loss in colors, which has a negative effect on subsequent object detection; therefore, single dehazing is challenging visual task. In addition, defects exist previous existing approaches: Pixel-based approaches are likely to result insufficient information estimate transmission, whereas patch-based ones prone generate shadows. They both also tend...

In this paper, we propose an efficient algorithm to directly restore a clear image from hazy input, which can be adapted for nighttime dehazing. The proposed hinges on trainable neural network realized in encoder–decoder architecture. encoder is exploited capture the context of derived input images, while decoder employed estimate contribution each final dehazed result using learned representat...

Objective The haze weather environment leads to the deterioration of visual effect image, and it is difficult carry out work advanced vision task. Therefore, dehazing image an important step before execution Traditional algorithms achieve by improving brightness contrast or constructing artificial priors such as color attenuation dark channel priors, but unstable when dealing with complex scene...