833 research outputs found
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
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
A Cascaded Convolutional Neural Network for Single Image Dehazing
Images captured under outdoor scenes usually suffer from low contrast and
limited visibility due to suspended atmospheric particles, which directly
affects the quality of photos. Despite numerous image dehazing methods have
been proposed, effective hazy image restoration remains a challenging problem.
Existing learning-based methods usually predict the medium transmission by
Convolutional Neural Networks (CNNs), but ignore the key global atmospheric
light. Different from previous learning-based methods, we propose a flexible
cascaded CNN for single hazy image restoration, which considers the medium
transmission and global atmospheric light jointly by two task-driven
subnetworks. Specifically, the medium transmission estimation subnetwork is
inspired by the densely connected CNN while the global atmospheric light
estimation subnetwork is a light-weight CNN. Besides, these two subnetworks are
cascaded by sharing the common features. Finally, with the estimated model
parameters, the haze-free image is obtained by the atmospheric scattering model
inversion, which achieves more accurate and effective restoration performance.
Qualitatively and quantitatively experimental results on the synthetic and
real-world hazy images demonstrate that the proposed method effectively removes
haze from such images, and outperforms several state-of-the-art dehazing
methods.Comment: This manuscript is accepted by IEEE ACCES
Joint Transmission Map Estimation and Dehazing using Deep Networks
Single image haze removal is an extremely challenging problem due to its
inherent ill-posed nature. Several prior-based and learning-based methods have
been proposed in the literature to solve this problem and they have achieved
superior results. However, most of the existing methods assume constant
atmospheric light model and tend to follow a two-step procedure involving
prior-based methods for estimating transmission map followed by calculation of
dehazed image using the closed form solution. In this paper, we relax the
constant atmospheric light assumption and propose a novel unified single image
dehazing network that jointly estimates the transmission map and performs
dehazing. In other words, our new approach provides an end-to-end learning
framework, where the inherent transmission map and dehazed result are learned
directly from the loss function. Extensive experiments on synthetic and real
datasets with challenging hazy images demonstrate that the proposed method
achieves significant improvements over the state-of-the-art methods.Comment: This paper has been accepted in IEEE-TCSV
"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors
Many seemingly unrelated computer vision tasks can be viewed as a special
case of image decomposition into separate layers. For example, image
segmentation (separation into foreground and background layers); transparent
layer separation (into reflection and transmission layers); Image dehazing
(separation into a clear image and a haze map), and more. In this paper we
propose a unified framework for unsupervised layer decomposition of a single
image, based on coupled "Deep-image-Prior" (DIP) networks. It was shown
[Ulyanov et al] that the structure of a single DIP generator network is
sufficient to capture the low-level statistics of a single image. We show that
coupling multiple such DIPs provides a powerful tool for decomposing images
into their basic components, for a wide variety of applications. This
capability stems from the fact that the internal statistics of a mixture of
layers is more complex than the statistics of each of its individual
components. We show the power of this approach for Image-Dehazing, Fg/Bg
Segmentation, Watermark-Removal, Transparency Separation in images and video,
and more. These capabilities are achieved in a totally unsupervised way, with
no training examples other than the input image/video itself.Comment: Project page: http://www.wisdom.weizmann.ac.il/~vision/DoubleDIP
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
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
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
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
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
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