2,234 research outputs found
An All-in-One Network for Dehazing and Beyond
This paper proposes an image dehazing model built with a convolutional neural
network (CNN), called All-in-One Dehazing Network (AOD-Net). It is designed
based on a re-formulated atmospheric scattering model. Instead of estimating
the transmission matrix and the atmospheric light separately as most previous
models did, AOD-Net directly generates the clean image through a light-weight
CNN. Such a novel end-to-end design makes it easy to embed AOD-Net into other
deep models, e.g., Faster R-CNN, for improving high-level task performance on
hazy images. Experimental results on both synthesized and natural hazy image
datasets demonstrate our superior performance than the state-of-the-art in
terms of PSNR, SSIM and the subjective visual quality. Furthermore, when
concatenating AOD-Net with Faster R-CNN and training the joint pipeline from
end to end, we witness a large improvement of the object detection performance
on hazy images
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
Benchmarking Single Image Dehazing and Beyond
We present a comprehensive study and evaluation of existing single image
dehazing algorithms, using a new large-scale benchmark consisting of both
synthetic and real-world hazy images, called REalistic Single Image DEhazing
(RESIDE). RESIDE highlights diverse data sources and image contents, and is
divided into five subsets, each serving different training or evaluation
purposes. We further provide a rich variety of criteria for dehazing algorithm
evaluation, ranging from full-reference metrics, to no-reference metrics, to
subjective evaluation and the novel task-driven evaluation. Experiments on
RESIDE shed light on the comparisons and limitations of state-of-the-art
dehazing algorithms, and suggest promising future directions.Comment: IEEE Transactions on Image Processing(TIP 2019
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
UG Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments
The UG challenge in IEEE CVPR 2019 aims to evoke a comprehensive
discussion and exploration about how low-level vision techniques can benefit
the high-level automatic visual recognition in various scenarios. In its second
track, we focus on object or face detection in poor visibility enhancements
caused by bad weathers (haze, rain) and low light conditions. While existing
enhancement methods are empirically expected to help the high-level end task,
that is observed to not always be the case in practice. To provide a more
thorough examination and fair comparison, we introduce three benchmark sets
collected in real-world hazy, rainy, and low-light conditions, respectively,
with annotate objects/faces annotated. To our best knowledge, this is the first
and currently largest effort of its kind. Baseline results by cascading
existing enhancement and detection models are reported, indicating the highly
challenging nature of our new data as well as the large room for further
technical innovations. We expect a large participation from the broad research
community to address these challenges together.Comment: A summary paper on datasets, fact sheets, baseline results, challenge
results, and winning methods in UG Challenge (Track 2). More materials
are provided in http://www.ug2challenge.org/index.htm
DR-Net: Transmission Steered Single Image Dehazing Network with Weakly Supervised Refinement
Despite the recent progress in image dehazing, several problems remain
largely unsolved such as robustness for varying scenes, the visual quality of
reconstructed images, and effectiveness and flexibility for applications. To
tackle these problems, we propose a new deep network architecture for single
image dehazing called DR-Net. Our model consists of three main subnetworks: a
transmission prediction network that predicts transmission map for the input
image, a haze removal network that reconstructs latent image steered by the
transmission map, and a refinement network that enhances the details and color
properties of the dehazed result via weakly supervised learning. Compared to
previous methods, our method advances in three aspects: (i) pure data-driven
model; (ii) the end-to-end system; (iii) superior robustness, accuracy, and
applicability. Extensive experiments demonstrate that our DR-Net outperforms
the state-of-the-art methods on both synthetic and real images in qualitative
and quantitative metrics. Additionally, the utility of DR-Net has been
illustrated by its potential usage in several important computer vision tasks.Comment: 8 pages, 8 figures, submitted to CVPR 201
End-to-End United Video Dehazing and Detection
The recent development of CNN-based image dehazing has revealed the
effectiveness of end-to-end modeling. However, extending the idea to end-to-end
video dehazing has not been explored yet. In this paper, we propose an
End-to-End Video Dehazing Network (EVD-Net), to exploit the temporal
consistency between consecutive video frames. A thorough study has been
conducted over a number of structure options, to identify the best temporal
fusion strategy. Furthermore, we build an End-to-End United Video Dehazing and
Detection Network(EVDD-Net), which concatenates and jointly trains EVD-Net with
a video object detection model. The resulting augmented end-to-end pipeline has
demonstrated much more stable and accurate detection results in hazy video
Input Dropout for Spatially Aligned Modalities
Computer vision datasets containing multiple modalities such as color, depth,
and thermal properties are now commonly accessible and useful for solving a
wide array of challenging tasks. However, deploying multi-sensor heads is not
possible in many scenarios. As such many practical solutions tend to be based
on simpler sensors, mostly for cost, simplicity and robustness considerations.
In this work, we propose a training methodology to take advantage of these
additional modalities available in datasets, even if they are not available at
test time. By assuming that the modalities have a strong spatial correlation,
we propose Input Dropout, a simple technique that consists in stochastic hiding
of one or many input modalities at training time, while using only the
canonical (e.g. RGB) modalities at test time. We demonstrate that Input Dropout
trivially combines with existing deep convolutional architectures, and improves
their performance on a wide range of computer vision tasks such as dehazing,
6-DOF object tracking, pedestrian detection and object classification.Comment: Accepted in ICIP 2020. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
future media, including reprinting/republishing this material for advertising
or promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other work
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
Incorporating Near-Infrared Information into Semantic Image Segmentation
Recent progress in computational photography has shown that we can acquire
near-infrared (NIR) information in addition to the normal visible (RGB) band,
with only slight modifications to standard digital cameras. Due to the
proximity of the NIR band to visible radiation, NIR images share many
properties with visible images. However, as a result of the material dependent
reflection in the NIR part of the spectrum, such images reveal different
characteristics of the scene. We investigate how to effectively exploit these
differences to improve performance on the semantic image segmentation task.
Based on a state-of-the-art segmentation framework and a novel manually
segmented image database (both indoor and outdoor scenes) that contain
4-channel images (RGB+NIR), we study how to best incorporate the specific
characteristics of the NIR response. We show that adding NIR leads to improved
performance for classes that correspond to a specific type of material in both
outdoor and indoor scenes. We also discuss the results with respect to the
physical properties of the NIR response
- …