287 research outputs found
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
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
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
Haze Visibility Enhancement: A Survey and Quantitative Benchmarking
This paper provides a comprehensive survey of methods dealing with visibility
enhancement of images taken in hazy or foggy scenes. The survey begins with
discussing the optical models of atmospheric scattering media and image
formation. This is followed by a survey of existing methods, which are grouped
to multiple image methods, polarizing filters based methods, methods with known
depth, and single-image methods. We also provide a benchmark of a number of
well known single-image methods, based on a recent dataset provided by Fattal
and our newly generated scattering media dataset that contains ground truth
images for quantitative evaluation. To our knowledge, this is the first
benchmark using numerical metrics to evaluate dehazing techniques. This
benchmark allows us to objectively compare the results of existing methods and
to better identify the strengths and limitations of each method
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
CGGAN: A Context Guided Generative Adversarial Network For Single Image Dehazing
Image haze removal is highly desired for the application of computer vision.
This paper proposes a novel Context Guided Generative Adversarial Network
(CGGAN) for single image dehazing. Of which, an novel new encoder-decoder is
employed as the generator. And it consists of a feature-extraction-net, a
context-extractionnet, and a fusion-net in sequence. The feature extraction-net
acts as a encoder, and is used for extracting haze features. The
context-extraction net is a multi-scale parallel pyramid decoder, and is used
for extracting the deep features of the encoder and generating coarse dehazing
image. The fusion-net is a decoder, and is used for obtaining the final
haze-free image. To obtain more better results, multi-scale information
obtained during the decoding process of the context extraction decoder is used
for guiding the fusion decoder. By introducing an extra coarse decoder to the
original encoder-decoder, the CGGAN can make better use of the deep feature
information extracted by the encoder. To ensure our CGGAN work effectively for
different haze scenarios, different loss functions are employed for the two
decoders. Experiments results show the advantage and the effectiveness of our
proposed CGGAN, evidential improvements over existing state-of-the-art methods
are obtained.Comment: 12 pages, 7 figures, 3 table
Single Image Haze Removal using a Generative Adversarial Network
Traditional methods to remove haze from images rely on estimating a
transmission map. When dealing with single images, this becomes an ill-posed
problem due to the lack of depth information. In this paper, we propose an
end-to-end learning based approach which uses a modified conditional Generative
Adversarial Network to directly remove haze from an image. We employ the usage
of the Tiramisu model in place of the classic U-Net model as the generator
owing to its higher parameter efficiency and performance. Moreover, a patch
based discriminator was used to reduce artefacts in the output. To further
improve the perceptual quality of the output, a hybrid weighted loss function
was designed and used to train the model. Experiments on synthetic and real
world hazy images demonstrates that our model performs competitively with the
state of the art methods.Comment: Accepted for the WiSPNET 2020 conference. Please refer to the GitHub
repository for information on updates to the paper:
https://github.com/thatbrguy/Dehaze-GA
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
O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images
Haze removal or dehazing is a challenging ill-posed problem that has drawn a
significant attention in the last few years. Despite this growing interest, the
scientific community is still lacking a reference dataset to evaluate
objectively and quantitatively the performance of proposed dehazing methods.
The few datasets that are currently considered, both for assessment and
training of learning-based dehazing techniques, exclusively rely on synthetic
hazy images. To address this limitation, we introduce the first outdoor scenes
database (named O-HAZE) composed of pairs of real hazy and corresponding
haze-free images. In practice, hazy images have been captured in presence of
real haze, generated by professional haze machines, and OHAZE contains 45
different outdoor scenes depicting the same visual content recorded in
haze-free and hazy conditions, under the same illumination parameters. To
illustrate its usefulness, O-HAZE is used to compare a representative set of
state-of-the-art dehazing techniques, using traditional image quality metrics
such as PSNR, SSIM and CIEDE2000. This reveals the limitations of current
techniques, and questions some of their underlying assumptions.Comment: arXiv admin note: text overlap with arXiv:1804.0509
- …