10,979 research outputs found
A Deep Journey into Super-resolution: A survey
Deep convolutional networks based super-resolution is a fast-growing field
with numerous practical applications. In this exposition, we extensively
compare 30+ state-of-the-art super-resolution Convolutional Neural Networks
(CNNs) over three classical and three recently introduced challenging datasets
to benchmark single image super-resolution. We introduce a taxonomy for
deep-learning based super-resolution networks that groups existing methods into
nine categories including linear, residual, multi-branch, recursive,
progressive, attention-based and adversarial designs. We also provide
comparisons between the models in terms of network complexity, memory
footprint, model input and output, learning details, the type of network losses
and important architectural differences (e.g., depth, skip-connections,
filters). The extensive evaluation performed, shows the consistent and rapid
growth in the accuracy in the past few years along with a corresponding boost
in model complexity and the availability of large-scale datasets. It is also
observed that the pioneering methods identified as the benchmark have been
significantly outperformed by the current contenders. Despite the progress in
recent years, we identify several shortcomings of existing techniques and
provide future research directions towards the solution of these open problems.Comment: Accepted in ACM Computing Survey
Efficient and Phase-aware Video Super-resolution for Cardiac MRI
Cardiac Magnetic Resonance Imaging (CMR) is widely used since it can
illustrate the structure and function of heart in a non-invasive and painless
way. However, it is time-consuming and high-cost to acquire the high-quality
scans due to the hardware limitation. To this end, we propose a novel
end-to-end trainable network to solve CMR video super-resolution problem
without the hardware upgrade and the scanning protocol modifications. We
incorporate the cardiac knowledge into our model to assist in utilizing the
temporal information. Specifically, we formulate the cardiac knowledge as the
periodic function, which is tailored to meet the cyclic characteristic of CMR.
In addition, the proposed residual of residual learning scheme facilitates the
network to learn the LR-HR mapping in a progressive refinement fashion. This
mechanism enables the network to have the adaptive capability by adjusting
refinement iterations depending on the difficulty of the task. Extensive
experimental results on large-scale datasets demonstrate the superiority of the
proposed method compared with numerous state-of-the-art methods.Comment: MICCAI 202
A Review of Convolutional Neural Networks for Inverse Problems in Imaging
In this survey paper, we review recent uses of convolution neural networks
(CNNs) to solve inverse problems in imaging. It has recently become feasible to
train deep CNNs on large databases of images, and they have shown outstanding
performance on object classification and segmentation tasks. Motivated by these
successes, researchers have begun to apply CNNs to the resolution of inverse
problems such as denoising, deconvolution, super-resolution, and medical image
reconstruction, and they have started to report improvements over
state-of-the-art methods, including sparsity-based techniques such as
compressed sensing. Here, we review the recent experimental work in these
areas, with a focus on the critical design decisions: Where does the training
data come from? What is the architecture of the CNN? and How is the learning
problem formulated and solved? We also bring together a few key theoretical
papers that offer perspective on why CNNs are appropriate for inverse problems
and point to some next steps in the field
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
Super-Resolution via Deep Learning
The recent phenomenal interest in convolutional neural networks (CNNs) must
have made it inevitable for the super-resolution (SR) community to explore its
potential. The response has been immense and in the last three years, since the
advent of the pioneering work, there appeared too many works not to warrant a
comprehensive survey. This paper surveys the SR literature in the context of
deep learning. We focus on the three important aspects of multimedia - namely
image, video and multi-dimensions, especially depth maps. In each case, first
relevant benchmarks are introduced in the form of datasets and state of the art
SR methods, excluding deep learning. Next is a detailed analysis of the
individual works, each including a short description of the method and a
critique of the results with special reference to the benchmarking done. This
is followed by minimum overall benchmarking in the form of comparison on some
common dataset, while relying on the results reported in various works
Deep Learning Techniques for Inverse Problems in Imaging
Recent work in machine learning shows that deep neural networks can be used
to solve a wide variety of inverse problems arising in computational imaging.
We explore the central prevailing themes of this emerging area and present a
taxonomy that can be used to categorize different problems and reconstruction
methods. Our taxonomy is organized along two central axes: (1) whether or not a
forward model is known and to what extent it is used in training and testing,
and (2) whether or not the learning is supervised or unsupervised, i.e.,
whether or not the training relies on access to matched ground truth image and
measurement pairs. We also discuss the trade-offs associated with these
different reconstruction approaches, caveats and common failure modes, plus
open problems and avenues for future work
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
Convolutional neural network (CNN) depth is of crucial importance for image
super-resolution (SR). However, we observe that deeper networks for image SR
are more difficult to train. The low-resolution inputs and features contain
abundant low-frequency information, which is treated equally across channels,
hence hindering the representational ability of CNNs. To solve these problems,
we propose the very deep residual channel attention networks (RCAN).
Specifically, we propose a residual in residual (RIR) structure to form very
deep network, which consists of several residual groups with long skip
connections. Each residual group contains some residual blocks with short skip
connections. Meanwhile, RIR allows abundant low-frequency information to be
bypassed through multiple skip connections, making the main network focus on
learning high-frequency information. Furthermore, we propose a channel
attention mechanism to adaptively rescale channel-wise features by considering
interdependencies among channels. Extensive experiments show that our RCAN
achieves better accuracy and visual improvements against state-of-the-art
methods.Comment: To appear in ECCV 201
Deep Learning for Image Super-resolution: A Survey
Image Super-Resolution (SR) is an important class of image processing
techniques to enhance the resolution of images and videos in computer vision.
Recent years have witnessed remarkable progress of image super-resolution using
deep learning techniques. This article aims to provide a comprehensive survey
on recent advances of image super-resolution using deep learning approaches. In
general, we can roughly group the existing studies of SR techniques into three
major categories: supervised SR, unsupervised SR, and domain-specific SR. In
addition, we also cover some other important issues, such as publicly available
benchmark datasets and performance evaluation metrics. Finally, we conclude
this survey by highlighting several future directions and open issues which
should be further addressed by the community in the future.Comment: Accepted by IEEE TPAM
A Survey of the Recent Architectures of Deep Convolutional Neural Networks
Deep Convolutional Neural Network (CNN) is a special type of Neural Networks,
which has shown exemplary performance on several competitions related to
Computer Vision and Image Processing. Some of the exciting application areas of
CNN include Image Classification and Segmentation, Object Detection, Video
Processing, Natural Language Processing, and Speech Recognition. The powerful
learning ability of deep CNN is primarily due to the use of multiple feature
extraction stages that can automatically learn representations from the data.
The availability of a large amount of data and improvement in the hardware
technology has accelerated the research in CNNs, and recently interesting deep
CNN architectures have been reported. Several inspiring ideas to bring
advancements in CNNs have been explored, such as the use of different
activation and loss functions, parameter optimization, regularization, and
architectural innovations. However, the significant improvement in the
representational capacity of the deep CNN is achieved through architectural
innovations. Notably, the ideas of exploiting spatial and channel information,
depth and width of architecture, and multi-path information processing have
gained substantial attention. Similarly, the idea of using a block of layers as
a structural unit is also gaining popularity. This survey thus focuses on the
intrinsic taxonomy present in the recently reported deep CNN architectures and,
consequently, classifies the recent innovations in CNN architectures into seven
different categories. These seven categories are based on spatial exploitation,
depth, multi-path, width, feature-map exploitation, channel boosting, and
attention. Additionally, the elementary understanding of CNN components,
current challenges, and applications of CNN are also provided.Comment: Number of Pages: 70, Number of Figures: 11, Number of Tables: 11.
Artif Intell Rev (2020
Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound
Contrast enhanced ultrasound is a radiation-free imaging modality which uses
encapsulated gas microbubbles for improved visualization of the vascular bed
deep within the tissue. It has recently been used to enable imaging with
unprecedented subwavelength spatial resolution by relying on super-resolution
techniques. A typical preprocessing step in super-resolution ultrasound is to
separate the microbubble signal from the cluttering tissue signal. This step
has a crucial impact on the final image quality. Here, we propose a new
approach to clutter removal based on robust principle component analysis (PCA)
and deep learning. We begin by modeling the acquired contrast enhanced
ultrasound signal as a combination of a low rank and sparse components. This
model is used in robust PCA and was previously suggested in the context of
ultrasound Doppler processing and dynamic magnetic resonance imaging. We then
illustrate that an iterative algorithm based on this model exhibits improved
separation of microbubble signal from the tissue signal over commonly practiced
methods. Next, we apply the concept of deep unfolding to suggest a deep network
architecture tailored to our clutter filtering problem which exhibits improved
convergence speed and accuracy with respect to its iterative counterpart. We
compare the performance of the suggested deep network on both simulations and
in-vivo rat brain scans, with a commonly practiced deep-network architecture
and the fast iterative shrinkage algorithm, and show that our architecture
exhibits better image quality and contrast
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