403 research outputs found
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
You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network
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 the ground-truth clean image (unsupervised) and a image
collection (untrained). An unsupervised neural network will avoid the intensive
labor collection of hazy-clean image pairs, and an untrained model is a
``real'' single image dehazing approach which could remove haze based on only
the observed hazy image itself and no extra images is used. Motivated by the
layer disentanglement idea, we propose a novel method, called you only look
yourself (\textbf{YOLY}) which could be one of the first unsupervised and
untrained neural networks for image dehazing. In brief, YOLY employs three
jointly subnetworks to separate the observed hazy image into several latent
layers, \textit{i.e.}, scene radiance layer, transmission map layer, and
atmospheric light layer. After that, these three layers are further composed to
the hazy image in a self-supervised manner. Thanks to the unsupervised and
untrained characteristics of YOLY, our method bypasses the conventional
training paradigm of deep models on hazy-clean pairs or a large scale dataset,
thus avoids the labor-intensive data collection and the domain shift issue.
Besides, our method also provides an effective learning-based haze transfer
solution thanks to its layer disentanglement mechanism. Extensive experiments
show the promising performance of our method in image dehazing compared with 14
methods on four databases
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
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
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
Progressive Feature Fusion Network for Realistic Image Dehazing
Single image dehazing is a challenging ill-posed restoration problem. Various
prior-based and learning-based methods have been proposed. Most of them follow
a classic atmospheric scattering model which is an elegant simplified physical
model based on the assumption of single-scattering and homogeneous atmospheric
medium. The formulation of haze in realistic environment is more complicated.
In this paper, we propose to take its essential mechanism as "black box", and
focus on learning an input-adaptive trainable end-to-end dehazing model. An
U-Net like encoder-decoder deep network via progressive feature fusions has
been proposed to directly learn highly nonlinear transformation function from
observed hazy image to haze-free ground-truth. The proposed network is
evaluated on two public image dehazing benchmarks. The experiments demonstrate
that it can achieve superior performance when compared with popular
state-of-the-art methods. With efficient GPU memory usage, it can
satisfactorily recover ultra high definition hazed image up to 4K resolution,
which is unaffordable by many deep learning based dehazing algorithms.Comment: 14 pages, 7 figures, 1 tables, accepted by ACCV201
Densely Connected Pyramid Dehazing Network
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, atmospheric light and dehazing all together. The end-to-end
learning is achieved by directly embedding the atmospheric scattering model
into the network, thereby ensuring that the proposed method strictly follows
the physics-driven scattering model for dehazing. Inspired by the dense network
that can maximize the information flow along features from different levels, we
propose a new edge-preserving densely connected encoder-decoder structure with
multi-level pyramid pooling module for estimating the transmission map. This
network is optimized using a newly introduced edge-preserving loss function. To
further incorporate the mutual structural information between the estimated
transmission map and the dehazed result, we propose a joint-discriminator based
on generative adversarial network framework to decide whether the corresponding
dehazed image and the estimated transmission map are real or fake. An ablation
study is conducted to demonstrate the effectiveness of each module evaluated at
both estimated transmission map and dehazed result. Extensive experiments
demonstrate that the proposed method achieves significant improvements over the
state-of-the-art methods. Code will be made available at:
https://github.com/hezhangsprinte
CGGAN: A Context Guided Generative Adversarial Network For Single Image Dehazing
Image haze removal is highly desired for the application of computer vision.
This paper proposes a novel Context Guided Generative Adversarial Network
(CGGAN) for single image dehazing. Of which, an novel new encoder-decoder is
employed as the generator. And it consists of a feature-extraction-net, a
context-extractionnet, and a fusion-net in sequence. The feature extraction-net
acts as a encoder, and is used for extracting haze features. The
context-extraction net is a multi-scale parallel pyramid decoder, and is used
for extracting the deep features of the encoder and generating coarse dehazing
image. The fusion-net is a decoder, and is used for obtaining the final
haze-free image. To obtain more better results, multi-scale information
obtained during the decoding process of the context extraction decoder is used
for guiding the fusion decoder. By introducing an extra coarse decoder to the
original encoder-decoder, the CGGAN can make better use of the deep feature
information extracted by the encoder. To ensure our CGGAN work effectively for
different haze scenarios, different loss functions are employed for the two
decoders. Experiments results show the advantage and the effectiveness of our
proposed CGGAN, evidential improvements over existing state-of-the-art methods
are obtained.Comment: 12 pages, 7 figures, 3 table
Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing
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 images for training. That is, we train the network
by feeding clean and hazy images in an unpaired manner. Moreover, the proposed
approach does not rely on estimation of the atmospheric scattering model
parameters. Our method enhances CycleGAN formulation by combining
cycle-consistency and perceptual losses in order to improve the quality of
textural information recovery and generate visually better haze-free images.
Typically, deep learning models for dehazing take low resolution images as
input and produce low resolution outputs. However, in the NTIRE 2018 challenge
on single image dehazing, high resolution images were provided. Therefore, we
apply bicubic downscaling. After obtaining low-resolution outputs from the
network, we utilize the Laplacian pyramid to upscale the output images to the
original resolution. We conduct experiments on NYU-Depth, I-HAZE, and O-HAZE
datasets. Extensive experiments demonstrate that the proposed approach improves
CycleGAN method both quantitatively and qualitatively.Comment: Accepted at CVPRW: NTIRE 201
Unsupervised Single Image Dehazing Using Dark Channel Prior Loss
Single image dehazing is a critical stage in many modern-day autonomous
vision applications. Early prior-based methods often involved a time-consuming
minimization of a hand-crafted energy function. Recent learning-based
approaches utilize the representational power of deep neural networks (DNNs) to
learn the underlying transformation between hazy and clear images. Due to
inherent limitations in collecting matching clear and hazy images, these
methods resort to training on synthetic data; constructed from indoor images
and corresponding depth information. This may result in a possible domain shift
when treating outdoor scenes. We propose a completely unsupervised method of
training via minimization of the well-known, Dark Channel Prior (DCP) energy
function. Instead of feeding the network with synthetic data, we solely use
real-world outdoor images and tune the network's parameters by directly
minimizing the DCP. Although our "Deep DCP" technique can be regarded as a fast
approximator of DCP, it actually improves its results significantly. This
suggests an additional regularization obtained via the network and learning
process. Experiments show that our method performs on par with large-scale
supervised methods
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