403 research outputs found
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
O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images
Haze removal or dehazing is a challenging ill-posed problem that has drawn a
significant attention in the last few years. Despite this growing interest, the
scientific community is still lacking a reference dataset to evaluate
objectively and quantitatively the performance of proposed dehazing methods.
The few datasets that are currently considered, both for assessment and
training of learning-based dehazing techniques, exclusively rely on synthetic
hazy images. To address this limitation, we introduce the first outdoor scenes
database (named O-HAZE) composed of pairs of real hazy and corresponding
haze-free images. In practice, hazy images have been captured in presence of
real haze, generated by professional haze machines, and OHAZE contains 45
different outdoor scenes depicting the same visual content recorded in
haze-free and hazy conditions, under the same illumination parameters. To
illustrate its usefulness, O-HAZE is used to compare a representative set of
state-of-the-art dehazing techniques, using traditional image quality metrics
such as PSNR, SSIM and CIEDE2000. This reveals the limitations of current
techniques, and questions some of their underlying assumptions.Comment: arXiv admin note: text overlap with arXiv:1804.0509
Single Image Dehazing through Improved Atmospheric Light Estimation
Image contrast enhancement for outdoor vision is important for smart car
auxiliary transport systems. The video frames captured in poor weather
conditions are often characterized by poor visibility. Most image dehazing
algorithms consider to use a hard threshold assumptions or user input to
estimate atmospheric light. However, the brightest pixels sometimes are objects
such as car lights or streetlights, especially for smart car auxiliary
transport systems. Simply using a hard threshold may cause a wrong estimation.
In this paper, we propose a single optimized image dehazing method that
estimates atmospheric light efficiently and removes haze through the estimation
of a semi-globally adaptive filter. The enhanced images are characterized with
little noise and good exposure in dark regions. The textures and edges of the
processed images are also enhanced significantly.Comment: Multimedia Tools and Applications (2015
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
Haze Visibility Enhancement: A Survey and Quantitative Benchmarking
This paper provides a comprehensive survey of methods dealing with visibility
enhancement of images taken in hazy or foggy scenes. The survey begins with
discussing the optical models of atmospheric scattering media and image
formation. This is followed by a survey of existing methods, which are grouped
to multiple image methods, polarizing filters based methods, methods with known
depth, and single-image methods. We also provide a benchmark of a number of
well known single-image methods, based on a recent dataset provided by Fattal
and our newly generated scattering media dataset that contains ground truth
images for quantitative evaluation. To our knowledge, this is the first
benchmark using numerical metrics to evaluate dehazing techniques. This
benchmark allows us to objectively compare the results of existing methods and
to better identify the strengths and limitations of each method
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
Single Image Haze Removal using a Generative Adversarial Network
Traditional methods to remove haze from images rely on estimating a
transmission map. When dealing with single images, this becomes an ill-posed
problem due to the lack of depth information. In this paper, we propose an
end-to-end learning based approach which uses a modified conditional Generative
Adversarial Network to directly remove haze from an image. We employ the usage
of the Tiramisu model in place of the classic U-Net model as the generator
owing to its higher parameter efficiency and performance. Moreover, a patch
based discriminator was used to reduce artefacts in the output. To further
improve the perceptual quality of the output, a hybrid weighted loss function
was designed and used to train the model. Experiments on synthetic and real
world hazy images demonstrates that our model performs competitively with the
state of the art methods.Comment: Accepted for the WiSPNET 2020 conference. Please refer to the GitHub
repository for information on updates to the paper:
https://github.com/thatbrguy/Dehaze-GA
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
Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset
Underwater images suffer from color distortion and low contrast, because
light is attenuated while it propagates through water. Attenuation under water
varies with wavelength, unlike terrestrial images where attenuation is assumed
to be spectrally uniform. The attenuation depends both on the water body and
the 3D structure of the scene, making color restoration difficult.
Unlike existing single underwater image enhancement techniques, our method
takes into account multiple spectral profiles of different water types. By
estimating just two additional global parameters: the attenuation ratios of the
blue-red and blue-green color channels, the problem is reduced to single image
dehazing, where all color channels have the same attenuation coefficients.
Since the water type is unknown, we evaluate different parameters out of an
existing library of water types. Each type leads to a different restored image
and the best result is automatically chosen based on color distribution.
We collected a dataset of images taken in different locations with varying
water properties, showing color charts in the scenes. Moreover, to obtain
ground truth, the 3D structure of the scene was calculated based on stereo
imaging. This dataset enables a quantitative evaluation of restoration
algorithms on natural images and shows the advantage of our method
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