243 research outputs found
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
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
Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing
In this paper, we present an end-to-end network, called Cycle-Dehaze, for
single image dehazing problem, which does not require pairs of hazy and
corresponding ground truth images for training. That is, we train the network
by feeding clean and hazy images in an unpaired manner. Moreover, the proposed
approach does not rely on estimation of the atmospheric scattering model
parameters. Our method enhances CycleGAN formulation by combining
cycle-consistency and perceptual losses in order to improve the quality of
textural information recovery and generate visually better haze-free images.
Typically, deep learning models for dehazing take low resolution images as
input and produce low resolution outputs. However, in the NTIRE 2018 challenge
on single image dehazing, high resolution images were provided. Therefore, we
apply bicubic downscaling. After obtaining low-resolution outputs from the
network, we utilize the Laplacian pyramid to upscale the output images to the
original resolution. We conduct experiments on NYU-Depth, I-HAZE, and O-HAZE
datasets. Extensive experiments demonstrate that the proposed approach improves
CycleGAN method both quantitatively and qualitatively.Comment: Accepted at CVPRW: NTIRE 201
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
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
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
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
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
A Smoke Removal Method for Laparoscopic Images
In laparoscopic surgery, image quality can be severely degraded by surgical
smoke, which not only introduces error for the image processing (used in image
guided surgery), but also reduces the visibility of the surgeons. In this
paper, we propose to enhance the laparoscopic images by decomposing them into
unwanted smoke part and enhanced part using a variational approach. The
proposed method relies on the observation that smoke has low contrast and low
inter-channel differences. A cost function is defined based on this prior
knowledge and is solved using an augmented Lagrangian method. The obtained
unwanted smoke component is then subtracted from the original degraded image,
resulting in the enhanced image. The obtained quantitative scores in terms of
FADE, JNBM and RE metrics show that our proposed method performs rather well.
Furthermore, the qualitative visual inspection of the results show that it
removes smoke effectively from the laparoscopic images
Does Haze Removal Help CNN-based Image Classification?
Hazy images are common in real scenarios and many dehazing methods have been
developed to automatically remove the haze from images. Typically, the goal of
image dehazing is to produce clearer images from which human vision can better
identify the object and structural details present in the images. When the
ground-truth haze-free image is available for a hazy image, quantitative
evaluation of image dehazing is usually based on objective metrics, such as
Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). However, in
many applications, large-scale images are collected not for visual examination
by human. Instead, they are used for many high-level vision tasks, such as
automatic classification, recognition and categorization. One fundamental
problem here is whether various dehazing methods can produce clearer images
that can help improve the performance of the high-level tasks. In this paper,
we empirically study this problem in the important task of image classification
by using both synthetic and real hazy image datasets. From the experimental
results, we find that the existing image-dehazing methods cannot improve much
the image-classification performance and sometimes even reduce the
image-classification performance
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