Many real-world situations such as bad weather may result in hazy environments. Images captured these conditions will have low image quality due to microparticles the air. The light scatter and absorb, resulting images with various effects. In recent years, dehazing has been researched depth handle conditions. Various methods were developed, from traditional deep learning methods. Traditional f...

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...

In this letter, we propose a novel and effective haze removal method by using the structure-aware atmospheric veil. More specifically, the initial atmospheric veil is first estimated based on dark channel prior and morphological operator. Furthermore, an energy optimization function considering the structure feature of the input image is constructed to refine the initial atmospheric veil. At la...

Fog and haze can have a dramatic impact on vision systems for land and sea vehicles. The impact of such conditions on infrared images is not as severe as for standard images. By fusing images from two cameras, one ordinary and one near-infrared camera, a complete dehazing system with colour preservation can be achieved. Applying several di erent algorithms to an image set and evaluating the res...

In this paper, we implement a dehazing algorithm using dark pixel detection in MatLab and C++/OpenCV. The algorithm is originally proposed in Yu et al.’s paper. The simplicity and effectiveness of the algorithm make it possible for us to run the C++/OpenCV implementation on Android phones through android NDK with reasonable speed. Keywords—dehaze; image processing; mobile application; computer ...