7,257 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
Effects of Image Degradations to CNN-based Image Classification
Just like many other topics in computer vision, image classification has
achieved significant progress recently by using deep-learning neural networks,
especially the Convolutional Neural Networks (CNN). Most of the existing works
are focused on classifying very clear natural images, evidenced by the widely
used image databases such as Caltech-256, PASCAL VOCs and ImageNet. However, in
many real applications, the acquired images may contain certain degradations
that lead to various kinds of blurring, noise, and distortions. One important
and interesting problem is the effect of such degradations to the performance
of CNN-based image classification. More specifically, we wonder whether
image-classification performance drops with each kind of degradation, whether
this drop can be avoided by including degraded images into training, and
whether existing computer vision algorithms that attempt to remove such
degradations can help improve the image-classification performance. In this
paper, we empirically study this problem for four kinds of degraded images --
hazy images, underwater images, motion-blurred images and fish-eye images. For
this study, we synthesize a large number of such degraded images by applying
respective physical models to the clear natural images and collect a new hazy
image dataset from the Internet. We expect this work can draw more interests
from the community to study the classification of degraded images
Tracking Live Fish from Low-Contrast and Low-Frame-Rate Stereo Videos
Non-extractive fish abundance estimation with the aid of visual analysis has
drawn increasing attention. Unstable illumination, ubiquitous noise and low
frame rate video capturing in the underwater environment, however, make
conventional tracking methods unreliable. In this paper, we present a multiple
fish tracking system for low-contrast and low-frame-rate stereo videos with the
use of a trawl-based underwater camera system. An automatic fish segmentation
algorithm overcomes the low-contrast issues by adopting a histogram
backprojection approach on double local-thresholded images to ensure an
accurate segmentation on the fish shape boundaries. Built upon a reliable
feature-based object matching method, a multiple-target tracking algorithm via
a modified Viterbi data association is proposed to overcome the poor motion
continuity and frequent entrance/exit of fish targets under low-frame-rate
scenarios. In addition, a computationally efficient block-matching approach
performs successful stereo matching, which enables an automatic fish-body tail
compensation to greatly reduce segmentation error and allows for an accurate
fish length measurement. Experimental results show that an effective and
reliable tracking performance for multiple live fish with underwater stereo
cameras is achieved.Comment: 14 pages, 14 figures, 6 table
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
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
Towards Real-Time Advancement of Underwater Visual Quality with GAN
Low visual quality has prevented underwater robotic vision from a wide range
of applications. Although several algorithms have been developed, real-time and
adaptive methods are deficient for real-world tasks. In this paper, we address
this difficulty based on generative adversarial networks (GAN), and propose a
GAN-based restoration scheme (GAN-RS). In particular, we develop a multi-branch
discriminator including an adversarial branch and a critic branch for the
purpose of simultaneously preserving image content and removing underwater
noise. In addition to adversarial learning, a novel dark channel prior loss
also promotes the generator to produce realistic vision. More specifically, an
underwater index is investigated to describe underwater properties, and a loss
function based on the underwater index is designed to train the critic branch
for underwater noise suppression. Through extensive comparisons on visual
quality and feature restoration, we confirm the superiority of the proposed
approach. Consequently, the GAN-RS can adaptively improve underwater visual
quality in real time and induce an overall superior restoration performance.
Finally, a real-world experiment is conducted on the seabed for grasping marine
products, and the results are quite promising. The source code is publicly
available at https://github.com/SeanChenxy/GAN_RS
Underwater Fish Tracking for Moving Cameras based on Deformable Multiple Kernels
Fishery surveys that call for the use of single or multiple underwater
cameras have been an emerging technology as a non-extractive mean to estimate
the abundance of fish stocks. Tracking live fish in an open aquatic environment
posts challenges that are different from general pedestrian or vehicle tracking
in surveillance applications. In many rough habitats fish are monitored by
cameras installed on moving platforms, where tracking is even more challenging
due to inapplicability of background models. In this paper, a novel tracking
algorithm based on the deformable multiple kernels (DMK) is proposed to address
these challenges. Inspired by the deformable part model (DPM) technique, a set
of kernels is defined to represent the holistic object and several parts that
are arranged in a deformable configuration. Color histogram, texture histogram
and the histogram of oriented gradients (HOG) are extracted and serve as object
features. Kernel motion is efficiently estimated by the mean-shift algorithm on
color and texture features to realize tracking. Furthermore, the HOG-feature
deformation costs are adopted as soft constraints on kernel positions to
maintain the part configuration. Experimental results on practical video set
from underwater moving cameras show the reliable performance of the proposed
method with much less computational cost comparing with state-of-the-art
techniques.Comment: 11 page
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
UID2021: An Underwater Image Dataset for Evaluation of No-reference Quality Assessment Metrics
Achieving subjective and objective quality assessment of underwater images is
of high significance in underwater visual perception and image/video
processing. However, the development of underwater image quality assessment
(UIQA) is limited for the lack of comprehensive human subjective user study
with publicly available dataset and reliable objective UIQA metric. To address
this issue, we establish a large-scale underwater image dataset, dubbed
UID2021, for evaluating no-reference UIQA metrics. The constructed dataset
contains 60 multiply degraded underwater images collected from various sources,
covering six common underwater scenes (i.e. bluish scene, bluish-green scene,
greenish scene, hazy scene, low-light scene, and turbid scene), and their
corresponding 900 quality improved versions generated by employing fifteen
state-of-the-art underwater image enhancement and restoration algorithms. Mean
opinion scores (MOS) for UID2021 are also obtained by using the pair comparison
sorting method with 52 observers. Both in-air NR-IQA and underwater-specific
algorithms are tested on our constructed dataset to fairly compare the
performance and analyze their strengths and weaknesses. Our proposed UID2021
dataset enables ones to evaluate NR UIQA algorithms comprehensively and paves
the way for further research on UIQA. Our UID2021 will be a free download and
utilized for research purposes at: https://github.com/Hou-Guojia/UID2021
Emerging from Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer
Underwater vision suffers from severe effects due to selective attenuation
and scattering when light propagates through water. Such degradation not only
affects the quality of underwater images but limits the ability of vision
tasks. Different from existing methods which either ignore the wavelength
dependency of the attenuation or assume a specific spectral profile, we tackle
color distortion problem of underwater image from a new view. In this letter,
we propose a weakly supervised color transfer method to correct color
distortion, which relaxes the need of paired underwater images for training and
allows for the underwater images unknown where were taken. Inspired by
Cycle-Consistent Adversarial Networks, we design a multi-term loss function
including adversarial loss, cycle consistency loss, and SSIM (Structural
Similarity Index Measure) loss, which allows the content and structure of the
corrected result the same as the input, but the color as if the image was taken
without the water. Experiments on underwater images captured under diverse
scenes show that our method produces visually pleasing results, even
outperforms the art-of-the-state methods. Besides, our method can improve the
performance of vision tasks.Comment: Submitted to IEEE Signal Processing Letter
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