1,623 research outputs found
Multiple Linear Regression Haze-removal Model Based on Dark Channel Prior
Dark Channel Prior (DCP) is a widely recognized traditional dehazing
algorithm. However, it may fail in bright region and the brightness of the
restored image is darker than hazy image. In this paper, we propose an
effective method to optimize DCP. We build a multiple linear regression
haze-removal model based on DCP atmospheric scattering model and train this
model with RESIDE dataset, which aims to reduce the unexpected errors caused by
the rough estimations of transmission map t(x) and atmospheric light A. The
RESIDE dataset provides enough synthetic hazy images and their corresponding
groundtruth images to train and test. We compare the performances of different
dehazing algorithms in terms of two important full-reference metrics, the
peak-signal-to-noise ratio (PSNR) as well as the structural similarity index
measure (SSIM). The experiment results show that our model gets highest SSIM
value and its PSNR value is also higher than most of state-of-the-art dehazing
algorithms. Our results also overcome the weakness of DCP on real-world hazy
imagesComment: IEEE CPS (CSCI 2018 Int'l Conference
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
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
Image Dehazing using Bilinear Composition Loss Function
In this paper, we introduce a bilinear composition loss function to address
the problem of image dehazing. Previous methods in image dehazing use a
two-stage approach which first estimate the transmission map followed by clear
image estimation. The drawback of a two-stage method is that it tends to boost
local image artifacts such as noise, aliasing and blocking. This is especially
the case for heavy haze images captured with a low quality device. Our method
is based on convolutional neural networks. Unique in our method is the bilinear
composition loss function which directly model the correlations between
transmission map, clear image, and atmospheric light. This allows errors to be
back-propagated to each sub-network concurrently, while maintaining the
composition constraint to avoid overfitting of each sub-network. We evaluate
the effectiveness of our proposed method using both synthetic and real world
examples. Extensive experiments show that our method outperfoms
state-of-the-art methods especially for haze images with severe noise level and
compressions
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
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
Analysis of Probabilistic multi-scale fractional order fusion-based de-hazing algorithm
In this report, a de-hazing algorithm based on probability and multi-scale
fractional order-based fusion is proposed. The proposed scheme improves on a
previously implemented multiscale fraction order-based fusion by augmenting its
local contrast and edge sharpening features. It also brightens de-hazed images,
while avoiding sky region over-enhancement. The results of the proposed
algorithm are analyzed and compared with existing methods from the literature
and indicate better performance in most cases.Comment: 22 pages, 8 figures, journal preprin
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
Challenges in video based object detection in maritime scenario using computer vision
This paper discusses the technical challenges in maritime image processing
and machine vision problems for video streams generated by cameras. Even well
documented problems of horizon detection and registration of frames in a video
are very challenging in maritime scenarios. More advanced problems of
background subtraction and object detection in video streams are very
challenging. Challenges arising from the dynamic nature of the background,
unavailability of static cues, presence of small objects at distant
backgrounds, illumination effects, all contribute to the challenges as
discussed here
Optical Observations of Gamma-Ray Bursts, the Discovery of Supernovae 2005bv, 2005ee, and 2006ak, and Searches for Transients Using the "MASTER" Robotic Telescope
We present the results of observations obtained using the MASTER robotic
telescope in 2005 - 2006, including the earliest observations of the optical
emission of the gamma-ray bursts GRB 050824 and GRB 060926. Together with later
observations, these data yield the brightness-variation law t^{-0.55+-0.05} for
GRB 050824. An optical flare was detected in GRB 060926 - a brightness
enhancement that repeated the behavior observed in the X-ray variations. The
spectrum of GRB 060926 is found to be F_E ~ E^-\beta, where \beta = 1.0+-0.2.
Limits on the optical brightnesses of 26 gamma-ray bursts have been derived, 9
of these for the first time. Data for more than 90% of the accessible sky down
to were taken and reduced in real time during the survey. A database has
been composed based on these data. Limits have been placed on the rate of
optical flares that are not associated with detected gamma-ray bursts, and on
the opening angle for the beams of gamma-ray bursts. Three new supernovae have
been discovered: SN 2005bv (type Ia) - the first to be discovered on Russian
territory, SN 2005ee - one of the most powerful type II supernovae known, and
SN 2006ak (type Ia). We have obtained an image of SN 2006X during the growth
stage and a light curve that fully describes the brightness maximum and
exponential decay. A new method for searching for optical transients of
gamma-ray bursts detected using triangulation from various spacecraft is
proposed and tested.Comment: 30 pages, 18 figures, 9 table
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