624 research outputs found
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
Physics-Based Generative Adversarial Models for Image Restoration and Beyond
We present an algorithm to directly solve numerous image restoration problems
(e.g., image deblurring, image dehazing, image deraining, etc.). These problems
are highly ill-posed, and the common assumptions for existing methods are
usually based on heuristic image priors. In this paper, we find that these
problems can be solved by generative models with adversarial learning. However,
the basic formulation of generative adversarial networks (GANs) does not
generate realistic images, and some structures of the estimated images are
usually not preserved well. Motivated by an interesting observation that the
estimated results should be consistent with the observed inputs under the
physics models, we propose a physics model constrained learning algorithm so
that it can guide the estimation of the specific task in the conventional GAN
framework. The proposed algorithm is trained in an end-to-end fashion and can
be applied to a variety of image restoration and related low-level vision
problems. Extensive experiments demonstrate that our method performs favorably
against the state-of-the-art algorithms.Comment: IEEE TPAM
Gated Fusion Network for Single Image Dehazing
In this paper, we propose an efficient algorithm to directly restore a clear
image from a hazy input. The proposed algorithm hinges on an end-to-end
trainable neural network that consists of an encoder and a decoder. The encoder
is exploited to capture the context of the derived input images, while the
decoder is employed to estimate the contribution of each input to the final
dehazed result using the learned representations attributed to the encoder. The
constructed network adopts a novel fusion-based strategy which derives three
inputs from an original hazy image by applying White Balance (WB), Contrast
Enhancing (CE), and Gamma Correction (GC). We compute pixel-wise confidence
maps based on the appearance differences between these different inputs to
blend the information of the derived inputs and preserve the regions with
pleasant visibility. The final dehazed image is yielded by gating the important
features of the derived inputs. To train the network, we introduce a
multi-scale approach such that the halo artifacts can be avoided. Extensive
experimental results on both synthetic and real-world images demonstrate that
the proposed algorithm performs favorably against the state-of-the-art
algorithms
Learning Dual Convolutional Neural Networks for Low-Level Vision
In this paper, we propose a general dual convolutional neural network
(DualCNN) for low-level vision problems, e.g., super-resolution,
edge-preserving filtering, deraining and dehazing. These problems usually
involve the estimation of two components of the target signals: structures and
details. Motivated by this, our proposed DualCNN consists of two parallel
branches, which respectively recovers the structures and details in an
end-to-end manner. The recovered structures and details can generate the target
signals according to the formation model for each particular application. The
DualCNN is a flexible framework for low-level vision tasks and can be easily
incorporated into existing CNNs. Experimental results show that the DualCNN can
be effectively applied to numerous low-level vision tasks with favorable
performance against the state-of-the-art methods.Comment: CVPR 201
The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing
We propose a novel deep neural network architecture for the challenging
problem of single image dehazing, which aims to recover the clear image from a
degraded hazy image. Instead of relying on hand-crafted image priors or
explicitly estimating the components of the widely used atmospheric scattering
model, our end-to-end system directly generates the clear image from an input
hazy image. The proposed network has an encoder-decoder architecture with skip
connections and instance normalization. We adopt the convolutional layers of
the pre-trained VGG network as encoder to exploit the representation power of
deep features, and demonstrate the effectiveness of instance normalization for
image dehazing. Our simple yet effective network outperforms the
state-of-the-art methods by a large margin on the benchmark datasets
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
Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing
Haze and smog are among the most common environmental factors impacting image
quality and, therefore, image analysis. This paper proposes an end-to-end
generative method for image dehazing. It is based on designing a fully
convolutional neural network to recognize haze structures in input images and
restore clear, haze-free images. The proposed method is agnostic in the sense
that it does not explore the atmosphere scattering model. Somewhat
surprisingly, it achieves superior performance relative to all existing
state-of-the-art methods for image dehazing even on SOTS outdoor images, which
are synthesized using the atmosphere scattering model.
Project detail and code can be found here:
https://github.com/Seanforfun/GMAN_Net_Haze_Remova
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
Fractional Multiscale Fusion-based De-hazing
This report presents the results of a proposed multi-scale fusion-based
single image de-hazing algorithm, which can also be used for underwater image
enhancement. Furthermore, the algorithm was designed for very fast operation
and minimal run-time. The proposed scheme is the faster than existing
algorithms for both de-hazing and underwater image enhancement and amenable to
digital hardware implementation. Results indicate mostly consistent and good
results for both categories of images when compared with other algorithms from
the literature.Comment: 23 pages, 13 figures, 2 table
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
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