2 research outputs found
Dense Scattering Layer Removal
We propose a new model, together with advanced optimization, to separate a
thick scattering media layer from a single natural image. It is able to handle
challenging underwater scenes and images taken in fog and sandstorm, both of
which are with significantly reduced visibility. Our method addresses the
critical issue -- this is, originally unnoticeable impurities will be greatly
magnified after removing the scattering media layer -- with transmission-aware
optimization. We introduce non-local structure-aware regularization to properly
constrain transmission estimation without introducing the halo artifacts. A
selective-neighbor criterion is presented to convert the unconventional
constrained optimization problem to an unconstrained one where the latter can
be efficiently solved.Comment: 10 pages, 10 figures, Siggraph Asia 2013 Technial Brief
Single Image Dehazing Using Ranking Convolutional Neural Network
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