15,691 research outputs found
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
Multi-Focus Image Fusion Using Sparse Representation and Coupled Dictionary Learning
We address the multi-focus image fusion problem, where multiple images
captured with different focal settings are to be fused into an all-in-focus
image of higher quality. Algorithms for this problem necessarily admit the
source image characteristics along with focused and blurred features. However,
most sparsity-based approaches use a single dictionary in focused feature space
to describe multi-focus images, and ignore the representations in blurred
feature space. We propose a multi-focus image fusion approach based on sparse
representation using a coupled dictionary. It exploits the observations that
the patches from a given training set can be sparsely represented by a couple
of overcomplete dictionaries related to the focused and blurred categories of
images and that a sparse approximation based on such coupled dictionary leads
to a more flexible and therefore better fusion strategy than the one based on
just selecting the sparsest representation in the original image estimate. In
addition, to improve the fusion performance, we employ a coupled dictionary
learning approach that enforces pairwise correlation between atoms of
dictionaries learned to represent the focused and blurred feature spaces. We
also discuss the advantages of the fusion approach based on coupled dictionary
learning, and present efficient algorithms for fusion based on coupled
dictionary learning. Extensive experimental comparisons with state-of-the-art
multi-focus image fusion algorithms validate the effectiveness of the proposed
approach.Comment: 25 pages, 15 figures, 2 tabl
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
Kernel based low-rank sparse model for single image super-resolution
Self-similarity learning has been recognized as a promising method for single
image super-resolution (SR) to produce high-resolution (HR) image in recent
years. The performance of learning based SR reconstruction, however, highly
depends on learned representation coeffcients. Due to the degradation of input
image, conventional sparse coding is prone to produce unfaithful representation
coeffcients. To this end, we propose a novel kernel based low-rank sparse model
with self-similarity learning for single image SR which incorporates
nonlocalsimilarity prior to enforce similar patches having similar
representation weights. We perform a gradual magnification scheme, using
self-examples extracted from the degraded input image and up-scaled versions.
To exploit nonlocal-similarity, we concatenate the vectorized input patch and
its nonlocal neighbors at different locations into a data matrix which consists
of similar components. Then we map the nonlocal data matrix into a
high-dimensional feature space by kernel method to capture their nonlinear
structures. Under the assumption that the sparse coeffcients for the nonlocal
data in the kernel space should be low-rank, we impose low-rank constraint on
sparse coding to share similarities among representation coeffcients and remove
outliers in order that stable weights for SR reconstruction can be obtained.
Experimental results demonstrate the advantage of our proposed method in both
visual quality and reconstruction error.Comment: 27 pages, Keywords: low-rank, sparse representation, kernel method,
self-similarity learning, super-resolutio
Image Super-Resolution Using TV Priori Guided Convolutional Network
We proposed a TV priori information guided deep learning method for single
image super-resolution(SR). The new alogorithm up-sample method based on TV
priori, new learning method and neural networks architecture are embraced in
our TV guided priori Convolutional Neural Network which diretcly learns an end
to end mapping between the low level to high level images.Comment: This paper is underviewring in Journal of Pattern Recognition Letter
Generative Adversarial Network in Medical Imaging: A Review
Generative adversarial networks have gained a lot of attention in the
computer vision community due to their capability of data generation without
explicitly modelling the probability density function. The adversarial loss
brought by the discriminator provides a clever way of incorporating unlabeled
samples into training and imposing higher order consistency. This has proven to
be useful in many cases, such as domain adaptation, data augmentation, and
image-to-image translation. These properties have attracted researchers in the
medical imaging community, and we have seen rapid adoption in many traditional
and novel applications, such as image reconstruction, segmentation, detection,
classification, and cross-modality synthesis. Based on our observations, this
trend will continue and we therefore conducted a review of recent advances in
medical imaging using the adversarial training scheme with the hope of
benefiting researchers interested in this technique.Comment: 24 pages; v4; added missing references from before Jan 1st 2019;
accepted to MedI
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images
Single Image Super Resolution (SISR) techniques based on Super Resolution
Convolutional Neural Networks (SRCNN) are applied to micro-computed tomography
({\mu}CT) images of sandstone and carbonate rocks. Digital rock imaging is
limited by the capability of the scanning device resulting in trade-offs
between resolution and field of view, and super resolution methods tested in
this study aim to compensate for these limits. SRCNN models SR-Resnet, Enhanced
Deep SR (EDSR), and Wide-Activation Deep SR (WDSR) are used on the Digital Rock
Super Resolution 1 (DRSRD1) Dataset of 4x downsampled images, comprising of
2000 high resolution (800x800) raw micro-CT images of Bentheimer sandstone and
Estaillades carbonate. The trained models are applied to the validation and
test data within the dataset and show a 3-5 dB rise in image quality compared
to bicubic interpolation, with all tested models performing within a 0.1 dB
range. Difference maps indicate that edge sharpness is completely recovered in
images within the scope of the trained model, with only high frequency noise
related detail loss. We find that aside from generation of high-resolution
images, a beneficial side effect of super resolution methods applied to
synthetically downgraded images is the removal of image noise while recovering
edgewise sharpness which is beneficial for the segmentation process. The model
is also tested against real low-resolution images of Bentheimer rock with image
augmentation to account for natural noise and blur. The SRCNN method is shown
to act as a preconditioner for image segmentation under these circumstances
which naturally leads to further future development and training of models that
segment an image directly. Image restoration by SRCNN on the rock images is of
significantly higher quality than traditional methods and suggests SRCNN
methods are a viable processing step in a digital rock workflow.Comment: 24 page
Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks
Deep learning methods, in particular, trained Convolutional Neural Networks
(CNN) have recently been shown to produce compelling results for single image
Super-Resolution (SR). Invariably, a CNN is learned to map the Low Resolution
(LR) image to its corresponding High Resolution (HR) version in the spatial
domain. We propose a novel network structure for learning the SR mapping
function in an image transform domain, specifically the Discrete Cosine
Transform (DCT). As the first contribution, we show that DCT can be integrated
into the network structure as a Convolutional DCT (CDCT) layer. With the CDCT
layer, we construct the DCT Deep SR (DCT-DSR) network. We further extend the
DCT-DSR to allow the CDCT layer to become trainable (i.e., optimizable).
Because this layer represents an image transform, we enforce pairwise
orthogonality constraints and newly formulated complexity order constraints on
the individual basis functions/filters. This Orthogonally Regularized Deep SR
network (ORDSR) simplifies the SR task by taking advantage of image transform
domain while adapting the design of transform basis to the training image set.
Experimental results show ORDSR achieves state-of-the-art SR image quality with
fewer parameters than most of the deep CNN methods. A particular success of
ORDSR is in overcoming the artifacts introduced by bicubic interpolation. A key
burden of deep SR has been identified as the requirement of generous training
LR and HR image pairs; ORSDR exhibits a much more graceful degradation as
training size is reduced with significant benefits in the regime of limited
training. Analysis of memory and computation requirements confirms that ORDSR
can allow for a more efficient network with faster inference
Medical Image Generation using Generative Adversarial Networks
Generative adversarial networks (GANs) are unsupervised Deep Learning
approach in the computer vision community which has gained significant
attention from the last few years in identifying the internal structure of
multimodal medical imaging data. The adversarial network simultaneously
generates realistic medical images and corresponding annotations, which proven
to be useful in many cases such as image augmentation, image registration,
medical image generation, image reconstruction, and image-to-image translation.
These properties bring the attention of the researcher in the field of medical
image analysis and we are witness of rapid adaption in many novel and
traditional applications. This chapter provides state-of-the-art progress in
GANs-based clinical application in medical image generation, and cross-modality
synthesis. The various framework of GANs which gained popularity in the
interpretation of medical images, such as Deep Convolutional GAN (DCGAN),
Laplacian GAN (LAPGAN), pix2pix, CycleGAN, and unsupervised image-to-image
translation model (UNIT), continue to improve their performance by
incorporating additional hybrid architecture, has been discussed. Further, some
of the recent applications of these frameworks for image reconstruction, and
synthesis, and future research directions in the area have been covered.Comment: 19 pages, 3 figures, 5 table
Channel Splitting Network for Single MR Image Super-Resolution
High resolution magnetic resonance (MR) imaging is desirable in many clinical
applications due to its contribution to more accurate subsequent analyses and
early clinical diagnoses. Single image super resolution (SISR) is an effective
and cost efficient alternative technique to improve the spatial resolution of
MR images. In the past few years, SISR methods based on deep learning
techniques, especially convolutional neural networks (CNNs), have achieved
state-of-the-art performance on natural images. However, the information is
gradually weakened and training becomes increasingly difficult as the network
deepens. The problem is more serious for medical images because lacking high
quality and effective training samples makes deep models prone to underfitting
or overfitting. Nevertheless, many current models treat the hierarchical
features on different channels equivalently, which is not helpful for the
models to deal with the hierarchical features discriminatively and targetedly.
To this end, we present a novel channel splitting network (CSN) to ease the
representational burden of deep models. The proposed CSN model divides the
hierarchical features into two branches, i.e., residual branch and dense
branch, with different information transmissions. The residual branch is able
to promote feature reuse, while the dense branch is beneficial to the
exploration of new features. Besides, we also adopt the merge-and-run mapping
to facilitate information integration between different branches. Extensive
experiments on various MR images, including proton density (PD), T1 and T2
images, show that the proposed CSN model achieves superior performance over
other state-of-the-art SISR methods.Comment: 13 pages, 11 figures and 4 table
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