  Single image dehazing, which aims to recover the clear image solely from an
input hazy or foggy image, is a challenging ill-posed problem. Analysing
existing approaches, the common key step is to estimate the haze density of
each pixel. To this end, various approaches often heuristically designed
haze-relevant features. Several recent works also automatically learn the
features via directly exploiting Convolutional Neural Networks (CNN). However,
it may be insufficient to fully capture the intrinsic attributes of hazy
images. To obtain effective features for single image dehazing, this paper
presents a novel Ranking Convolutional Neural Network (Ranking-CNN). In
Ranking-CNN, a novel ranking layer is proposed to extend the structure of CNN
so that the statistical and structural attributes of hazy images can be
simultaneously captured. By training Ranking-CNN in a well-designed manner,
powerful haze-relevant features can be automatically learned from massive hazy
image patches. Based on these features, haze can be effectively removed by
using a haze density prediction model trained through the random forest
regression. Experimental results show that our approach outperforms several
previous dehazing approaches on synthetic and real-world benchmark images.
Comprehensive analyses are also conducted to interpret the proposed Ranking-CNN
from both the theoretical and experimental aspects.
