20,708 research outputs found
Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions
Underwater image enhancement is such an important low-level vision task with
many applications that numerous algorithms have been proposed in recent years.
These algorithms developed upon various assumptions demonstrate successes from
various aspects using different data sets and different metrics. In this work,
we setup an undersea image capturing system, and construct a large-scale
Real-world Underwater Image Enhancement (RUIE) data set divided into three
subsets. The three subsets target at three challenging aspects for enhancement,
i.e., image visibility quality, color casts, and higher-level
detection/classification, respectively. We conduct extensive and systematic
experiments on RUIE to evaluate the effectiveness and limitations of various
algorithms to enhance visibility and correct color casts on images with
hierarchical categories of degradation. Moreover, underwater image enhancement
in practice usually serves as a preprocessing step for mid-level and high-level
vision tasks. We thus exploit the object detection performance on enhanced
images as a brand new task-specific evaluation criterion. The findings from
these evaluations not only confirm what is commonly believed, but also suggest
promising solutions and new directions for visibility enhancement, color
correction, and object detection on real-world underwater images.Comment: arXiv admin note: text overlap with arXiv:1712.04143 by other author
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
An Image Based Technique for Enhancement of Underwater Images
The underwater images usually suffers from non-uniform lighting, low
contrast, blur and diminished colors. In this paper, we proposed an image based
preprocessing technique to enhance the quality of the underwater images. The
proposed technique comprises a combination of four filters such as homomorphic
filtering, wavelet denoising, bilateral filter and contrast equalization. These
filters are applied sequentially on degraded underwater images. The literature
survey reveals that image based preprocessing algorithms uses standard filter
techniques with various combinations. For smoothing the image, the image based
preprocessing algorithms uses the anisotropic filter. The main drawback of the
anisotropic filter is that iterative in nature and computation time is high
compared to bilateral filter. In the proposed technique, in addition to other
three filters, we employ a bilateral filter for smoothing the image. The
experimentation is carried out in two stages. In the first stage, we have
conducted various experiments on captured images and estimated optimal
parameters for bilateral filter. Similarly, optimal filter bank and optimal
wavelet shrinkage function are estimated for wavelet denoising. In the second
stage, we conducted the experiments using estimated optimal parameters, optimal
filter bank and optimal wavelet shrinkage function for evaluating the proposed
technique. We evaluated the technique using quantitative based criteria such as
a gradient magnitude histogram and Peak Signal to Noise Ratio (PSNR). Further,
the results are qualitatively evaluated based on edge detection results. The
proposed technique enhances the quality of the underwater images and can be
employed prior to apply computer vision techniques
Fast Underwater Image Enhancement for Improved Visual Perception
In this paper, we present a conditional generative adversarial network-based
model for real-time underwater image enhancement. To supervise the adversarial
training, we formulate an objective function that evaluates the perceptual
image quality based on its global content, color, local texture, and style
information. We also present EUVP, a large-scale dataset of a paired and
unpaired collection of underwater images (of `poor' and `good' quality) that
are captured using seven different cameras over various visibility conditions
during oceanic explorations and human-robot collaborative experiments. In
addition, we perform several qualitative and quantitative evaluations which
suggest that the proposed model can learn to enhance underwater image quality
from both paired and unpaired training. More importantly, the enhanced images
provide improved performances of standard models for underwater object
detection, human pose estimation, and saliency prediction. These results
validate that it is suitable for real-time preprocessing in the autonomy
pipeline by visually-guided underwater robots. The model and associated
training pipelines are available at https://github.com/xahidbuffon/funie-gan
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
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
Night Time Haze and Glow Removal using Deep Dilated Convolutional Network
In this paper, we address the single image haze removal problem in a
nighttime scene. The night haze removal is a severely ill-posed problem
especially due to the presence of various visible light sources with varying
colors and non-uniform illumination. These light sources are of different
shapes and introduce noticeable glow in night scenes. To address these effects
we introduce a deep learning based DeGlow-DeHaze iterative architecture which
accounts for varying color illumination and glows. First, our convolution
neural network (CNN) based DeGlow model is able to remove the glow effect
significantly and on top of it a separate DeHaze network is included to remove
the haze effect. For our recurrent network training, the hazy images and the
corresponding transmission maps are synthesized from the NYU depth datasets and
consequently restored a high-quality haze-free image. The experimental results
demonstrate that our hybrid CNN model outperforms other state-of-the-art
methods in terms of computation speed and image quality. We also show the
effectiveness of our model on a number of real images and compare our results
with the existing night haze heuristic models.Comment: 13 pages, 10 figures, 2 Table
Fast Single Image Dehazing via Multilevel Wavelet Transform based Optimization
The quality of images captured in outdoor environments can be affected by
poor weather conditions such as fog, dust, and atmospheric scattering of other
particles. This problem can bring extra challenges to high-level computer
vision tasks like image segmentation and object detection. However, previous
studies on image dehazing suffer from a huge computational workload and
corruption of the original image, such as over-saturation and halos. In this
paper, we present a novel image dehazing approach based on the optical model
for haze images and regularized optimization. Specifically, we convert the
non-convex, bilinear problem concerning the unknown haze-free image and light
transmission distribution to a convex, linear optimization problem by
estimating the atmosphere light constant. Our method is further accelerated by
introducing a multilevel Haar wavelet transform. The optimization, instead, is
applied to the low frequency sub-band decomposition of the original image. This
dimension reduction significantly improves the processing speed of our method
and exhibits the potential for real-time applications. Experimental results
show that our approach outperforms state-of-the-art dehazing algorithms in
terms of both image reconstruction quality and computational efficiency. For
implementation details, source code can be publicly accessed via
http://github.com/JiaxiHe/Image-and-Video-Dehazing.Comment: 23 pages, 13 figure
Single Image Restoration for Participating Media Based on Prior Fusion
This paper describes a method to restore degraded images captured in a
participating media -- fog, turbid water, sand storm, etc. Differently from the
related work that only deal with a medium, we obtain generality by using an
image formation model and a fusion of new image priors. The model considers the
image color variation produced by the medium. The proposed restoration method
is based on the fusion of these priors and supported by statistics collected on
images acquired in both non-participating and participating media. The key of
the method is to fuse two complementary measures --- local contrast and color
data. The obtained results on underwater and foggy images demonstrate the
capabilities of the proposed method. Moreover, we evaluated our method using a
special dataset for which a ground-truth image is available.Comment: This paper is under consideration at Pattern Recognition Letter
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
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