11,315 research outputs found
Generic 3D Convolutional Fusion for image restoration
Also recently, exciting strides forward have been made in the area of image
restoration, particularly for image denoising and single image
super-resolution. Deep learning techniques contributed to this significantly.
The top methods differ in their formulations and assumptions, so even if their
average performance may be similar, some work better on certain image types and
image regions than others. This complementarity motivated us to propose a novel
3D convolutional fusion (3DCF) method. Unlike other methods adapted to
different tasks, our method uses the exact same convolutional network
architecture to address both image denois- ing and single image
super-resolution. As a result, our 3DCF method achieves substantial
improvements (0.1dB-0.4dB PSNR) over the state-of-the-art methods that it
fuses, and this on standard benchmarks for both tasks. At the same time, the
method still is computationally efficient
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
NTIRE 2020 Challenge on Image and Video Deblurring
Motion blur is one of the most common degradation artifacts in dynamic scene
photography. This paper reviews the NTIRE 2020 Challenge on Image and Video
Deblurring. In this challenge, we present the evaluation results from 3
competition tracks as well as the proposed solutions. Track 1 aims to develop
single-image deblurring methods focusing on restoration quality. On Track 2,
the image deblurring methods are executed on a mobile platform to find the
balance of the running speed and the restoration accuracy. Track 3 targets
developing video deblurring methods that exploit the temporal relation between
input frames. In each competition, there were 163, 135, and 102 registered
participants and in the final testing phase, 9, 4, and 7 teams competed. The
winning methods demonstrate the state-ofthe-art performance on image and video
deblurring tasks.Comment: To be published in CVPR 2020 Workshop (New Trends in Image
Restoration and Enhancement
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
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
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
Face Recognition in Low Quality Images: A Survey
Low-resolution face recognition (LRFR) has received increasing attention over
the past few years. Its applications lie widely in the real-world environment
when high-resolution or high-quality images are hard to capture. One of the
biggest demands for LRFR technologies is video surveillance. As the the number
of surveillance cameras in the city increases, the videos that captured will
need to be processed automatically. However, those videos or images are usually
captured with large standoffs, arbitrary illumination condition, and diverse
angles of view. Faces in these images are generally small in size. Several
studies addressed this problem employed techniques like super resolution,
deblurring, or learning a relationship between different resolution domains. In
this paper, we provide a comprehensive review of approaches to low-resolution
face recognition in the past five years. First, a general problem definition is
given. Later, systematically analysis of the works on this topic is presented
by catogory. In addition to describing the methods, we also focus on datasets
and experiment settings. We further address the related works on unconstrained
low-resolution face recognition and compare them with the result that use
synthetic low-resolution data. Finally, we summarized the general limitations
and speculate a priorities for the future effort.Comment: There are some mistakes addressing in this paper which will be
misleading to the reader and we wont have a new version in short time. We
will resubmit once it is being corecte
A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening
Pan-sharpening is a fundamental and significant task in the field of remote
sensing imagery processing, in which high-resolution spatial details from
panchromatic images are employed to enhance the spatial resolution of
multi-spectral (MS) images. As the transformation from low spatial resolution
MS image to high-resolution MS image is complex and highly non-linear, inspired
by the powerful representation for non-linear relationships of deep neural
networks, we introduce multi-scale feature extraction and residual learning
into the basic convolutional neural network (CNN) architecture and propose the
multi-scale and multi-depth convolutional neural network (MSDCNN) for the
pan-sharpening of remote sensing imagery. Both the quantitative assessment
results and the visual assessment confirm that the proposed network yields
high-resolution MS images that are superior to the images produced by the
compared state-of-the-art methods
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
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning
Single image super resolution (SR), which refers to reconstruct a
higher-resolution (HR) image from the observed low-resolution (LR) image, has
received substantial attention due to its tremendous application potentials.
Despite the breakthroughs of recently proposed SR methods using convolutional
neural networks (CNNs), their generated results usually lack of preserving
structural (high-frequency) details. In this paper, regarding global boundary
context and residual context as complimentary information for enhancing
structural details in image restoration, we develop a contextualized multi-task
learning framework to address the SR problem. Specifically, our method first
extracts convolutional features from the input LR image and applies one
deconvolutional module to interpolate the LR feature maps in a content-adaptive
way. Then, the resulting feature maps are fed into two branched sub-networks.
During the neural network training, one sub-network outputs salient image
boundaries and the HR image, and the other sub-network outputs the local
residual map, i.e., the residual difference between the generated HR image and
ground-truth image. On several standard benchmarks (i.e., Set5, Set14 and
BSD200), our extensive evaluations demonstrate the effectiveness of our SR
method on achieving both higher restoration quality and computational
efficiency compared with several state-of-the-art SR approaches. The source
code and some SR results can be found at:
http://hcp.sysu.edu.cn/structure-preserving-image-super-resolution/Comment: To appear in Transactions on Multimedia 201
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