Images acquired in poor weather conditions (haze, fog, smog, mist, etc.) are often severely
degraded. In the atmosphere, there exists two types of particles: dry particles (dust, smoke …

Single image dehazing is a challenging ill-posed problem due to the severe information
degeneration. However, existing deep learning based dehazing methods only adopt clear …

Existing dehazing approaches struggle to process real-world hazy images owing to the lack
of paired real data and robust priors. In this work, we present a new paradigm for real image …

Deep learning-based methods have achieved remarkable performance for image dehazing.
However, previous studies are mostly focused on training models with synthetic hazy …

Image dehazing using learning-based methods has achieved state-of-the-art performance in
recent years. However, most existing methods train a dehazing model on synthetic hazy …

With the development of convolutional neural networks, hundreds of deep learning–based
dehazing methods have been proposed. In this article, we provide a comprehensive survey …

In this paper, we present an extensive review on deep learning methods for image
restoration tasks. Deep learning techniques, led by convolutional neural networks, have …

Enhancing the quality of low-light (LOL) images plays a very important role in many image
processing and multimedia applications. In recent years, a variety of deep learning …

Hazy images captured under ill-posed scenarios with scattering medium (ie haze, fog, or
smoke) are contaminated in visibility. Inevitably, these images are further degraded by …

Saturation information in hazy images is conducive to effective haze removal, However,
existing saturation-based dehazing methods just focus on the saturation value of each pixel …