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 …

Generative adversarial networks (GANs) have drawn enormous attention due to their simple
yet effective training mechanism and superior image generation quality. With the ability to …

Haze-free images are the prerequisites of many vision systems and algorithms, and thus
single image dehazing is of paramount importance in computer vision. In this field, prior …

In this paper, we reduce the image dehazing problem to an image-to-image translation
problem, and propose Enhanced Pix2pix Dehazing Network (EPDN), which generates a …

Recent successes in Generative Adversarial Networks (GAN) have affirmed the importance
of using more data in GAN training. Yet it is expensive to collect data in many domains such …

Owing to the fast developments of economics, a lot of devices and objects have been
connected and have formed the Internet of Things (IoT). Visual sensors have been applied …

Sparse coding-based anomaly detection has shown promising performance, of which the
keys are feature learning, sparse representation, and dictionary learning. In this paper, we …

In this paper, we study two challenging and less-touched problems in single image
dehazing, namely, how to make deep learning achieve image dehazing without training on …

Learning graphs from data automatically have shown encouraging performance on
clustering and semisupervised learning tasks. However, real data are often corrupted, which …

Magnetic resonance (MR) imaging plays an important role in clinical and brain exploration.
However, limited by factors such as imaging hardware, scanning time, and cost, it is …