6,648 research outputs found
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
Deep Depth Completion of a Single RGB-D Image
The goal of our work is to complete the depth channel of an RGB-D image.
Commodity-grade depth cameras often fail to sense depth for shiny, bright,
transparent, and distant surfaces. To address this problem, we train a deep
network that takes an RGB image as input and predicts dense surface normals and
occlusion boundaries. Those predictions are then combined with raw depth
observations provided by the RGB-D camera to solve for depths for all pixels,
including those missing in the original observation. This method was chosen
over others (e.g., inpainting depths directly) as the result of extensive
experiments with a new depth completion benchmark dataset, where holes are
filled in training data through the rendering of surface reconstructions
created from multiview RGB-D scans. Experiments with different network inputs,
depth representations, loss functions, optimization methods, inpainting
methods, and deep depth estimation networks show that our proposed approach
provides better depth completions than these alternatives.Comment: Accepted by CVPR2018 (Spotlight). Project webpage:
http://deepcompletion.cs.princeton.edu/ This version includes supplementary
materials which provide more implementation details, quantitative evaluation,
and qualitative results. Due to file size limit, please check project website
for high-res pape
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
Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images
Single image dehazing is an ill-posed problem that has recently drawn
important attention. Despite the significant increase in interest shown for
dehazing over the past few years, the validation of the dehazing methods
remains largely unsatisfactory, due to the lack of pairs of real hazy and
corresponding haze-free reference images. To address this limitation, we
introduce Dense-Haze - a novel dehazing dataset. Characterized by dense and
homogeneous hazy scenes, Dense-Haze contains 33 pairs of real hazy and
corresponding haze-free images of various outdoor scenes. The hazy scenes have
been recorded by introducing real haze, generated by professional haze
machines. The hazy and haze-free corresponding scenes contain the same visual
content captured under the same illumination parameters. Dense-Haze dataset
aims to push significantly the state-of-the-art in single-image dehazing by
promoting robust methods for real and various hazy scenes. We also provide a
comprehensive qualitative and quantitative evaluation of state-of-the-art
single image dehazing techniques based on the Dense-Haze dataset. Not
surprisingly, our study reveals that the existing dehazing techniques perform
poorly for dense homogeneous hazy scenes and that there is still much room for
improvement.Comment: 5 pages, 2 figure
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
Dense Scattering Layer Removal
We propose a new model, together with advanced optimization, to separate a
thick scattering media layer from a single natural image. It is able to handle
challenging underwater scenes and images taken in fog and sandstorm, both of
which are with significantly reduced visibility. Our method addresses the
critical issue -- this is, originally unnoticeable impurities will be greatly
magnified after removing the scattering media layer -- with transmission-aware
optimization. We introduce non-local structure-aware regularization to properly
constrain transmission estimation without introducing the halo artifacts. A
selective-neighbor criterion is presented to convert the unconventional
constrained optimization problem to an unconstrained one where the latter can
be efficiently solved.Comment: 10 pages, 10 figures, Siggraph Asia 2013 Technial Brief
Survey: Machine Learning in Production Rendering
In the past few years, machine learning-based approaches have had some great
success for rendering animated feature films. This survey summarizes several of
the most dramatic improvements in using deep neural networks over traditional
rendering methods, such as better image quality and lower computational
overhead. More specifically, this survey covers the fundamental principles of
machine learning and its applications, such as denoising, path guiding,
rendering participating media, and other notoriously difficult light transport
situations. Some of these techniques have already been used in the latest
released animations while others are still in the continuing development by
researchers in both academia and movie studios. Although learning-based
rendering methods still have some open issues, they have already demonstrated
promising performance in multiple parts of the rendering pipeline, and people
are continuously making new attempts.Comment: This was the survey I did for my PhD research exa
cvpaper.challenge in 2015 - A review of CVPR2015 and DeepSurvey
The "cvpaper.challenge" is a group composed of members from AIST, Tokyo Denki
Univ. (TDU), and Univ. of Tsukuba that aims to systematically summarize papers
on computer vision, pattern recognition, and related fields. For this
particular review, we focused on reading the ALL 602 conference papers
presented at the CVPR2015, the premier annual computer vision event held in
June 2015, in order to grasp the trends in the field. Further, we are proposing
"DeepSurvey" as a mechanism embodying the entire process from the reading
through all the papers, the generation of ideas, and to the writing of paper.Comment: Survey Pape
Embedding Bilateral Filter in Least Squares for Efficient Edge-preserving Image Smoothing
Edge-preserving smoothing is a fundamental procedure for many computer vision
and graphic applications. This can be achieved with either local methods or
global methods. In most cases, global methods can yield superior performance
over local ones. However, local methods usually run much faster than global
ones. In this paper, we propose a new global method that embeds the bilateral
filter in the least squares model for efficient edge-preserving smoothing. The
proposed method can show comparable performance with the state-of-the-art
global method. Meanwhile, since the proposed method can take advantages of the
efficiency of the bilateral filter and least squares model, it runs much
faster. In addition, we show the flexibility of our method which can be easily
extended by replacing the bilateral filter with its variants. They can be
further modified to handle more applications. We validate the effectiveness and
efficiency of the proposed method through comprehensive experiments in a range
of applications.Comment: accepted by TCSV
A Face Fairness Framework for 3D Meshes
In this paper, we present a face fairness framework for 3D meshes that
preserves the regular shape of faces and is applicable to a variety of 3D mesh
restoration tasks. Specifically, we present a number of desirable properties
for any mesh restoration method and show that our framework satisfies them. We
then apply our framework to two different tasks --- mesh-denoising and
mesh-refinement, and present comparative results for these two tasks showing
improvement over other relevant methods in the literature.Comment: 15 page
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