227 research outputs found
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
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
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
With widespread applications of artificial intelligence (AI), the
capabilities of the perception, understanding, decision-making and control for
autonomous systems have improved significantly in the past years. When
autonomous systems consider the performance of accuracy and transferability,
several AI methods, like adversarial learning, reinforcement learning (RL) and
meta-learning, show their powerful performance. Here, we review the
learning-based approaches in autonomous systems from the perspectives of
accuracy and transferability. Accuracy means that a well-trained model shows
good results during the testing phase, in which the testing set shares a same
task or a data distribution with the training set. Transferability means that
when a well-trained model is transferred to other testing domains, the accuracy
is still good. Firstly, we introduce some basic concepts of transfer learning
and then present some preliminaries of adversarial learning, RL and
meta-learning. Secondly, we focus on reviewing the accuracy or transferability
or both of them to show the advantages of adversarial learning, like generative
adversarial networks (GANs), in typical computer vision tasks in autonomous
systems, including image style transfer, image superresolution, image
deblurring/dehazing/rain removal, semantic segmentation, depth estimation,
pedestrian detection and person re-identification (re-ID). Then, we further
review the performance of RL and meta-learning from the aspects of accuracy or
transferability or both of them in autonomous systems, involving pedestrian
tracking, robot navigation and robotic manipulation. Finally, we discuss
several challenges and future topics for using adversarial learning, RL and
meta-learning in autonomous systems
Improved underwater image enhancement algorithms based on partial differential equations (PDEs)
The experimental results of improved underwater image enhancement algorithms
based on partial differential equations (PDEs) are presented in this report.
This second work extends the study of previous work and incorporating several
improvements into the revised algorithm. Experiments show the evidence of the
improvements when compared to previously proposed approaches and other
conventional algorithms found in the literature.Comment: 22 pages, 6 figure
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
The Next Best Underwater View
To image in high resolution large and occlusion-prone scenes, a camera must
move above and around. Degradation of visibility due to geometric occlusions
and distances is exacerbated by scattering, when the scene is in a
participating medium. Moreover, underwater and in other media, artificial
lighting is needed. Overall, data quality depends on the observed surface,
medium and the time-varying poses of the camera and light source. This work
proposes to optimize camera/light poses as they move, so that the surface is
scanned efficiently and the descattered recovery has the highest quality. The
work generalizes the next best view concept of robot vision to scattering media
and cooperative movable lighting. It also extends descattering to platforms
that move optimally. The optimization criterion is information gain, taken from
information theory. We exploit the existence of a prior rough 3D model, since
underwater such a model is routinely obtained using sonar. We demonstrate this
principle in a scaled-down setup
Joint Defogging and Demosaicking
Image defogging is a technique used extensively for enhancing visual quality
of images in bad weather condition. Even though defogging algorithms have been
well studied, defogging performance is degraded by demosaicking artifacts and
sensor noise amplification in distant scenes. In order to improve visual
quality of restored images, we propose a novel approach to perform defogging
and demosaicking simultaneously. We conclude that better defogging performance
with fewer artifacts can be achieved when a defogging algorithm is combined
with a demosaicking algorithm simultaneously. We also demonstrate that the
proposed joint algorithm has the benefit of suppressing noise amplification in
distant scene. In addition, we validate our theoretical analysis and
observations for both synthesized datasets with ground truth fog-free images
and natural scene datasets captured in a raw format
Sky detection and log illumination refinement for PDE-based hazy image contrast enhancement
This report presents the results of a sky detection technique used to improve
the performance of a previously developed partial differential equation
(PDE)-based hazy image enhancement algorithm. Additionally, a proposed
alternative method utilizes a function for log illumination refinement to
improve de-hazing results while avoiding over-enhancement of sky or homogeneous
regions. The algorithms were tested with several benchmark and calibration
images and compared with several standard algorithms from the literature.
Results indicate that the algorithms yield mostly consistent results and
surpasses several of the other algorithms in terms of colour and contrast
enhancement in addition to improved edge visibility.Comment: 22 pages, 13 figures, 5 table
Measuring Visibility using Atmospheric Transmission and Digital Surface Model
Reliable and exact assessment of visibility is essential for safe air
traffic. In order to overcome the drawbacks of the currently subjective reports
from human observers, we present an approach to automatically derive visibility
measures by means of image processing. It first exploits image based estimation
of the atmospheric transmission describing the portion of the light that is not
scattered by atmospheric phenomena (e.g., haze, fog, smoke) and reaches the
camera. Once the atmospheric transmission is estimated, a 3D representation of
the vicinity (digital surface model: DMS) is used to compute depth measurements
for the haze-free pixels and then derive a global visibility estimation for the
airport. Results on foggy images demonstrate the validity of the proposed
method.Comment: Presented at OAGM Workshop, 2015 (arXiv:1505.01065
Deep Dense Multi-scale Network for Snow Removal Using Semantic and Geometric Priors
Images captured in snowy days suffer from noticeable degradation of scene
visibility, which degenerates the performance of current vision-based
intelligent systems. Removing snow from images thus is an important topic in
computer vision. In this paper, we propose a Deep Dense Multi-Scale Network
(\textbf{DDMSNet}) for snow removal by exploiting semantic and geometric
priors. As images captured in outdoor often share similar scenes and their
visibility varies with depth from camera, such semantic and geometric
information provides a strong prior for snowy image restoration. We incorporate
the semantic and geometric maps as input and learn the semantic-aware and
geometry-aware representation to remove snow. In particular, we first create a
coarse network to remove snow from the input images. Then, the coarsely
desnowed images are fed into another network to obtain the semantic and
geometric labels. Finally, we design a DDMSNet to learn semantic-aware and
geometry-aware representation via a self-attention mechanism to produce the
final clean images. Experiments evaluated on public synthetic and real-world
snowy images verify the superiority of the proposed method, offering better
results both quantitatively and qualitatively
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