157,848 research outputs found
A Comprehensive Review of Deep Learning-based Single Image Super-resolution
Image super-resolution (SR) is one of the vital image processing methods that
improve the resolution of an image in the field of computer vision. In the last
two decades, significant progress has been made in the field of
super-resolution, especially by utilizing deep learning methods. This survey is
an effort to provide a detailed survey of recent progress in single-image
super-resolution in the perspective of deep learning while also informing about
the initial classical methods used for image super-resolution. The survey
classifies the image SR methods into four categories, i.e., classical methods,
supervised learning-based methods, unsupervised learning-based methods, and
domain-specific SR methods. We also introduce the problem of SR to provide
intuition about image quality metrics, available reference datasets, and SR
challenges. Deep learning-based approaches of SR are evaluated using a
reference dataset. Some of the reviewed state-of-the-art image SR methods
include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN),
multiscale residual network (MSRN), meta residual dense network (Meta-RDN),
recurrent back-projection network (RBPN), second-order attention network (SAN),
SR feedback network (SRFBN) and the wavelet-based residual attention network
(WRAN). Finally, this survey is concluded with future directions and trends in
SR and open problems in SR to be addressed by the researchers.Comment: 56 Pages, 11 Figures, 5 Table
Infrared Image Super-Resolution: Systematic Review, and Future Trends
Image Super-Resolution (SR) is essential for a wide range of computer vision
and image processing tasks. Investigating infrared (IR) image (or thermal
images) super-resolution is a continuing concern within the development of deep
learning. This survey aims to provide a comprehensive perspective of IR image
super-resolution, including its applications, hardware imaging system dilemmas,
and taxonomy of image processing methodologies. In addition, the datasets and
evaluation metrics in IR image super-resolution tasks are also discussed.
Furthermore, the deficiencies in current technologies and possible promising
directions for the community to explore are highlighted. To cope with the rapid
development in this field, we intend to regularly update the relevant excellent
work at \url{https://github.com/yongsongH/Infrared_Image_SR_SurveyComment: Submitted to IEEE TNNL
A Study on Super-Resolution Image Reconstruction Techniques
With the rapid development of space technology and its related technologies, more and more remote sensing platforms are sent to outer space to survey our earth. Recognizing and positioning all these space objects is the basis of knowing about the space, but there are no other effective methods in space target recognition except orbit and radio signal recognition. Super-resolution image reconstruction, which is based on the image of space objects, provides an effective way of solving this problem. In this paper, the principle of super-resolution image reconstruction and several typical reconstruction methods were introduced. By comparison, Nonparametric Finite Support Restoration Techniques were analyzed in details. At last, several aspects of super-resolution image reconstruction that should be studied further more were put forward
Deep learning based single image super-resolution : a survey
Single image super-resolution has attracted increasing attention and has a wide range of applications in satellite imaging, medical imaging, computer vision, security surveillance imaging, remote sensing, objection detection, and recognition. Recently, deep learning techniques have emerged and blossomed, producing “the state-of-the-art” in many domains. Due to their capability in feature extraction and mapping, it is very helpful to predict high-frequency details lost in low-resolution images. In this paper, we give an overview of recent advances in deep learning-based models and methods that have been applied to single image super-resolution tasks. We also summarize, compare and discuss various models from the past and present for comprehensive understanding and finally provide open problems and possible directions for future research
Constructing a WISE High Resolution Galaxy Atlas
After eight months of continuous observations, the Wide-field Infrared Survey
Explorer (WISE) mapped the entire sky at 3.4 {\mu}m, 4.6 {\mu}m, 12 {\mu}m and
22 {\mu}m. We have begun a dedicated WISE High Resolution Galaxy Atlas (WHRGA)
project to fully characterize large, nearby galaxies and produce a legacy image
atlas and source catalogue. Here we summarize the deconvolution technique used
to significantly improve the spatial resolution of WISE imaging, specifically
designed to study the internal anatomy of nearby galaxies. As a case study, we
present results for the galaxy NGC 1566, comparing the WISE super-resolution
image processing to that of Spitzer, GALEX and ground-based imaging. The is the
first paper in a two part series; results for a much larger sample of nearby
galaxies is presented in the second paper.Comment: Published in the AJ (2012, AJ, 144, 68
Deep Radio Interferometric Imaging with POLISH: DSA-2000 and weak lensing
Radio interferometry allows astronomers to probe small spatial scales that
are often inaccessible with single-dish instruments. However, recovering the
radio sky from an interferometer is an ill-posed deconvolution problem that
astronomers have worked on for half a century. More challenging still is
achieving resolution below the array's diffraction limit, known as
super-resolution imaging. To this end, we have developed a new learning-based
approach for radio interferometric imaging, leveraging recent advances in the
classical computer vision problems of single-image super-resolution (SISR) and
deconvolution. We have developed and trained a high dynamic range residual
neural network to learn the mapping between the dirty image and the true radio
sky. We call this procedure POLISH, in contrast to the traditional CLEAN
algorithm. The feed forward nature of learning-based approaches like POLISH is
critical for analyzing data from the upcoming Deep Synoptic Array (DSA-2000).
We show that POLISH achieves super-resolution, and we demonstrate its ability
to deconvolve real observations from the Very Large Array (VLA).
Super-resolution on DSA-2000 will allow us to measure the shapes and
orientations of several hundred million star forming radio galaxies (SFGs),
making it a powerful cosmological weak lensing survey and probe of dark energy.
We forecast its ability to constrain the lensing power spectrum, finding that
it will be complementary to next-generation optical surveys such as Euclid
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