733 research outputs found
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
Does Haze Removal Help CNN-based Image Classification?
Hazy images are common in real scenarios and many dehazing methods have been
developed to automatically remove the haze from images. Typically, the goal of
image dehazing is to produce clearer images from which human vision can better
identify the object and structural details present in the images. When the
ground-truth haze-free image is available for a hazy image, quantitative
evaluation of image dehazing is usually based on objective metrics, such as
Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). However, in
many applications, large-scale images are collected not for visual examination
by human. Instead, they are used for many high-level vision tasks, such as
automatic classification, recognition and categorization. One fundamental
problem here is whether various dehazing methods can produce clearer images
that can help improve the performance of the high-level tasks. In this paper,
we empirically study this problem in the important task of image classification
by using both synthetic and real hazy image datasets. From the experimental
results, we find that the existing image-dehazing methods cannot improve much
the image-classification performance and sometimes even reduce the
image-classification performance
A Cascaded Convolutional Neural Network for Single Image Dehazing
Images captured under outdoor scenes usually suffer from low contrast and
limited visibility due to suspended atmospheric particles, which directly
affects the quality of photos. Despite numerous image dehazing methods have
been proposed, effective hazy image restoration remains a challenging problem.
Existing learning-based methods usually predict the medium transmission by
Convolutional Neural Networks (CNNs), but ignore the key global atmospheric
light. Different from previous learning-based methods, we propose a flexible
cascaded CNN for single hazy image restoration, which considers the medium
transmission and global atmospheric light jointly by two task-driven
subnetworks. Specifically, the medium transmission estimation subnetwork is
inspired by the densely connected CNN while the global atmospheric light
estimation subnetwork is a light-weight CNN. Besides, these two subnetworks are
cascaded by sharing the common features. Finally, with the estimated model
parameters, the haze-free image is obtained by the atmospheric scattering model
inversion, which achieves more accurate and effective restoration performance.
Qualitatively and quantitatively experimental results on the synthetic and
real-world hazy images demonstrate that the proposed method effectively removes
haze from such images, and outperforms several state-of-the-art dehazing
methods.Comment: This manuscript is accepted by IEEE ACCES
An All-in-One Network for Dehazing and Beyond
This paper proposes an image dehazing model built with a convolutional neural
network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed
based on a re-formulated atmospheric scattering model. Instead of estimating
the transmission matrix and the atmospheric light separately as most previous
models did, AOD-Net directly generates the clean image through a light-weight
CNN. Such a novel end-to-end design makes it easy to embed AOD-Net into other
deep models, e.g., Faster R-CNN, for improving high-level task performance on
hazy images. Experimental results on both synthesized and natural hazy image
datasets demonstrate our superior performance than the state-of-the-art in
terms of PSNR, SSIM and the subjective visual quality. Furthermore, when
concatenating AOD-Net with Faster R-CNN and training the joint pipeline from
end to end, we witness a large improvement of the object detection performance
on hazy images
Joint Transmission Map Estimation and Dehazing using Deep Networks
Single image haze removal is an extremely challenging problem due to its
inherent ill-posed nature. Several prior-based and learning-based methods have
been proposed in the literature to solve this problem and they have achieved
superior results. However, most of the existing methods assume constant
atmospheric light model and tend to follow a two-step procedure involving
prior-based methods for estimating transmission map followed by calculation of
dehazed image using the closed form solution. In this paper, we relax the
constant atmospheric light assumption and propose a novel unified single image
dehazing network that jointly estimates the transmission map and performs
dehazing. In other words, our new approach provides an end-to-end learning
framework, where the inherent transmission map and dehazed result are learned
directly from the loss function. Extensive experiments on synthetic and real
datasets with challenging hazy images demonstrate that the proposed method
achieves significant improvements over the state-of-the-art methods.Comment: This paper has been accepted in IEEE-TCSV
End-to-End United Video Dehazing and Detection
The recent development of CNN-based image dehazing has revealed the
effectiveness of end-to-end modeling. However, extending the idea to end-to-end
video dehazing has not been explored yet. In this paper, we propose an
End-to-End Video Dehazing Network (EVD-Net), to exploit the temporal
consistency between consecutive video frames. A thorough study has been
conducted over a number of structure options, to identify the best temporal
fusion strategy. Furthermore, we build an End-to-End United Video Dehazing and
Detection Network(EVDD-Net), which concatenates and jointly trains EVD-Net with
a video object detection model. The resulting augmented end-to-end pipeline has
demonstrated much more stable and accurate detection results in hazy video
DR-Net: Transmission Steered Single Image Dehazing Network with Weakly Supervised Refinement
Despite the recent progress in image dehazing, several problems remain
largely unsolved such as robustness for varying scenes, the visual quality of
reconstructed images, and effectiveness and flexibility for applications. To
tackle these problems, we propose a new deep network architecture for single
image dehazing called DR-Net. Our model consists of three main subnetworks: a
transmission prediction network that predicts transmission map for the input
image, a haze removal network that reconstructs latent image steered by the
transmission map, and a refinement network that enhances the details and color
properties of the dehazed result via weakly supervised learning. Compared to
previous methods, our method advances in three aspects: (i) pure data-driven
model; (ii) the end-to-end system; (iii) superior robustness, accuracy, and
applicability. Extensive experiments demonstrate that our DR-Net outperforms
the state-of-the-art methods on both synthetic and real images in qualitative
and quantitative metrics. Additionally, the utility of DR-Net has been
illustrated by its potential usage in several important computer vision tasks.Comment: 8 pages, 8 figures, submitted to CVPR 201
Night Time Haze and Glow Removal using Deep Dilated Convolutional Network
In this paper, we address the single image haze removal problem in a
nighttime scene. The night haze removal is a severely ill-posed problem
especially due to the presence of various visible light sources with varying
colors and non-uniform illumination. These light sources are of different
shapes and introduce noticeable glow in night scenes. To address these effects
we introduce a deep learning based DeGlow-DeHaze iterative architecture which
accounts for varying color illumination and glows. First, our convolution
neural network (CNN) based DeGlow model is able to remove the glow effect
significantly and on top of it a separate DeHaze network is included to remove
the haze effect. For our recurrent network training, the hazy images and the
corresponding transmission maps are synthesized from the NYU depth datasets and
consequently restored a high-quality haze-free image. The experimental results
demonstrate that our hybrid CNN model outperforms other state-of-the-art
methods in terms of computation speed and image quality. We also show the
effectiveness of our model on a number of real images and compare our results
with the existing night haze heuristic models.Comment: 13 pages, 10 figures, 2 Table
Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images
Single image dehazing is an ill-posed problem that has recently drawn
important attention. Despite the significant increase in interest shown for
dehazing over the past few years, the validation of the dehazing methods
remains largely unsatisfactory, due to the lack of pairs of real hazy and
corresponding haze-free reference images. To address this limitation, we
introduce Dense-Haze - a novel dehazing dataset. Characterized by dense and
homogeneous hazy scenes, Dense-Haze contains 33 pairs of real hazy and
corresponding haze-free images of various outdoor scenes. The hazy scenes have
been recorded by introducing real haze, generated by professional haze
machines. The hazy and haze-free corresponding scenes contain the same visual
content captured under the same illumination parameters. Dense-Haze dataset
aims to push significantly the state-of-the-art in single-image dehazing by
promoting robust methods for real and various hazy scenes. We also provide a
comprehensive qualitative and quantitative evaluation of state-of-the-art
single image dehazing techniques based on the Dense-Haze dataset. Not
surprisingly, our study reveals that the existing dehazing techniques perform
poorly for dense homogeneous hazy scenes and that there is still much room for
improvement.Comment: 5 pages, 2 figure
UG Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments
The UG challenge in IEEE CVPR 2019 aims to evoke a comprehensive
discussion and exploration about how low-level vision techniques can benefit
the high-level automatic visual recognition in various scenarios. In its second
track, we focus on object or face detection in poor visibility enhancements
caused by bad weathers (haze, rain) and low light conditions. While existing
enhancement methods are empirically expected to help the high-level end task,
that is observed to not always be the case in practice. To provide a more
thorough examination and fair comparison, we introduce three benchmark sets
collected in real-world hazy, rainy, and low-light conditions, respectively,
with annotate objects/faces annotated. To our best knowledge, this is the first
and currently largest effort of its kind. Baseline results by cascading
existing enhancement and detection models are reported, indicating the highly
challenging nature of our new data as well as the large room for further
technical innovations. We expect a large participation from the broad research
community to address these challenges together.Comment: A summary paper on datasets, fact sheets, baseline results, challenge
results, and winning methods in UG Challenge (Track 2). More materials
are provided in http://www.ug2challenge.org/index.htm
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