Removing adverse weather conditions like rain, fog, and snow from images is an important
problem in many applications. Most methods proposed in the literature have been designed …

We propose a new end-to-end single image dehazing method, called Densely Connected
Pyramid Dehazing Network (DCPDN), which can jointly learn the transmission map …

Single image rain streak removal is an extremely challenging problem due to the presence
of non-uniform rain densities in images. We present a novel density-aware multi-stream …

This work addresses the problem of semantic foggy scene understanding (SFSU). Although
extensive research has been performed on image dehazing and on semantic scene …

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 …

Existing deep learning based de-raining approaches have resorted to the convolutional
architectures. However, the intrinsic limitations of convolution, including local receptive fields …

We present a novel method called Contextual Pyramid CNN (CP-CNN) for generating high-
quality crowd density and count estimation by explicitly incorporating global and local …

In this paper, we present an end-to-end network, called Cycle-Dehaze, for single image
dehazing problem, which does not require pairs of hazy and corresponding ground truth …

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