179 research outputs found
Feedback Graph Attention Convolutional Network for Medical Image Enhancement
Artifacts, blur and noise are the common distortions degrading MRI images
during the acquisition process, and deep neural networks have been demonstrated
to help in improving image quality. To well exploit global structural
information and texture details, we propose a novel biomedical image
enhancement network, named Feedback Graph Attention Convolutional Network
(FB-GACN). As a key innovation, we consider the global structure of an image by
building a graph network from image sub-regions that we consider to be node
features, linking them non-locally according to their similarity. The proposed
model consists of three main parts: 1) The parallel graph similarity branch and
content branch, where the graph similarity branch aims at exploiting the
similarity and symmetry across different image sub-regions in low-resolution
feature space and provides additional priors for the content branch to enhance
texture details. 2) A feedback mechanism with a recurrent structure to refine
low-level representations with high-level information and generate powerful
high-level texture details by handling the feedback connections. 3) A
reconstruction to remove the artifacts and recover super-resolution images by
using the estimated sub-region correlation priors obtained from the graph
similarity branch. We evaluate our method on two image enhancement tasks: i)
cross-protocol super resolution of diffusion MRI; ii) artifact removal of FLAIR
MR images. Experimental results demonstrate that the proposed algorithm
outperforms the state-of-the-art methods.Comment: The description of the experiments is not accurate and complete, and
some details of equations and expressions should be correcte
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution
This work identifies and addresses two important technical challenges in
single-image super-resolution: (1) how to upsample an image without magnifying
noise and (2) how to preserve large scale structure when upsampling. We
summarize the techniques we developed for our second place entry in Track 1
(Bicubic Downsampling), seventh place entry in Track 2 (Realistic Adverse
Conditions), and seventh place entry in Track 3 (Realistic difficult) in the
2018 NTIRE Super-Resolution Challenge. Furthermore, we present new neural
network architectures that specifically address the two challenges listed
above: denoising and preservation of large-scale structure.Comment: 8 pages, CVPR workshop 201
Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution
Recently, image super-resolution has been widely studied and achieved
significant progress by leveraging the power of deep convolutional neural
networks. However, there has been limited advancement in video super-resolution
(VSR) due to the complex temporal patterns in videos. In this paper, we
investigate how to adapt state-of-the-art methods of image super-resolution for
video super-resolution. The proposed adapting method is straightforward. The
information among successive frames is well exploited, while the overhead on
the original image super-resolution method is negligible. Furthermore, we
propose a learning-based method to ensemble the outputs from multiple
super-resolution models. Our methods show superior performance and rank second
in the NTIRE2019 Video Super-Resolution Challenge Track 1
GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise Modeling
Recent research on image denoising has progressed with the development of
deep learning architectures, especially convolutional neural networks. However,
real-world image denoising is still very challenging because it is not possible
to obtain ideal pairs of ground-truth images and real-world noisy images. Owing
to the recent release of benchmark datasets, the interest of the image
denoising community is now moving toward the real-world denoising problem. In
this paper, we propose a grouped residual dense network (GRDN), which is an
extended and generalized architecture of the state-of-the-art residual dense
network (RDN). The core part of RDN is defined as grouped residual dense block
(GRDB) and used as a building module of GRDN. We experimentally show that the
image denoising performance can be significantly improved by cascading GRDBs.
In addition to the network architecture design, we also develop a new
generative adversarial network-based real-world noise modeling method. We
demonstrate the superiority of the proposed methods by achieving the highest
score in terms of both the peak signal-to-noise ratio and the structural
similarity in the NTIRE2019 Real Image Denoising Challenge - Track 2:sRGB.Comment: To appear in CVPR 2019 workshop. The winners of the NTIRE2019
Challenge on Image Denoising Challenge: Track 2 sRG
Hierarchical Neural Architecture Search for Single Image Super-Resolution
Deep neural networks have exhibited promising performance in image
super-resolution (SR). Most SR models follow a hierarchical architecture that
contains both the cell-level design of computational blocks and the
network-level design of the positions of upsampling blocks. However, designing
SR models heavily relies on human expertise and is very labor-intensive. More
critically, these SR models often contain a huge number of parameters and may
not meet the requirements of computation resources in real-world applications.
To address the above issues, we propose a Hierarchical Neural Architecture
Search (HNAS) method to automatically design promising architectures with
different requirements of computation cost. To this end, we design a
hierarchical SR search space and propose a hierarchical controller for
architecture search. Such a hierarchical controller is able to simultaneously
find promising cell-level blocks and network-level positions of upsampling
layers. Moreover, to design compact architectures with promising performance,
we build a joint reward by considering both the performance and computation
cost to guide the search process. Extensive experiments on five benchmark
datasets demonstrate the superiority of our method over existing methods.Comment: This paper is accepted by IEEE Signal Processing Letter
Super-Resolved Image Perceptual Quality Improvement via Multi-Feature Discriminators
Generative adversarial network (GAN) for image super-resolution (SR) has
attracted enormous interests in recent years. However, the GAN-based SR methods
only use image discriminator to distinguish SR images and high-resolution (HR)
images. Image discriminator fails to discriminate images accurately since image
features cannot be fully expressed. In this paper, we design a new GAN-based SR
framework GAN-IMC which includes generator, image discriminator, morphological
component discriminator and color discriminator. The combination of multiple
feature discriminators improves the accuracy of image discrimination.
Adversarial training between the generator and multi-feature discriminators
forces SR images to converge with HR images in terms of data and features
distribution. Moreover, in some cases, feature enhancement of salient regions
is also worth considering. GAN-IMC is further optimized by weighted content
loss (GAN-IMCW), which effectively restores and enhances salient regions in SR
images. The effectiveness and robustness of our method are confirmed by
extensive experiments on public datasets. Compared with state-of-the-art
methods, the proposed method not only achieves competitive Perceptual Index
(PI) and Natural Image Quality Evaluator (NIQE) values but also obtains
pleasant visual perception in image edge, texture, color and salient regions.Comment: 18 pages, 10 figures, 6 table
Rain O'er Me: Synthesizing real rain to derain with data distillation
We present a supervised technique for learning to remove rain from images
without using synthetic rain software. The method is based on a two-stage data
distillation approach: 1) A rainy image is first paired with a coarsely
derained version using on a simple filtering technique ("rain-to-clean"). 2)
Then a clean image is randomly matched with the rainy soft-labeled pair.
Through a shared deep neural network, the rain that is removed from the first
image is then added to the clean image to generate a second pair
("clean-to-rain"). The neural network simultaneously learns to map both images
such that high resolution structure in the clean images can inform the
deraining of the rainy images. Demonstrations show that this approach can
address those visual characteristics of rain not easily synthesized by software
in the usual way
Dense-Resolution Network for Point Cloud Classification and Segmentation
Point cloud analysis is attracting attention from Artificial Intelligence
research since it can be widely used in applications such as robotics,
Augmented Reality, self-driving. However, it is always challenging due to
irregularities, unorderedness, and sparsity. In this article, we propose a
novel network named Dense-Resolution Network (DRNet) for point cloud analysis.
Our DRNet is designed to learn local point features from the point cloud in
different resolutions. In order to learn local point groups more effectively,
we present a novel grouping method for local neighborhood searching and an
error-minimizing module for capturing local features. In addition to validating
the network on widely used point cloud segmentation and classification
benchmarks, we also test and visualize the performance of the components.
Comparing with other state-of-the-art methods, our network shows superiority on
ModelNet40, ShapeNet synthetic and ScanObjectNN real point cloud datasets.Comment: To appear in WACV2021. Codes and models are available at:
https://github.com/ShiQiu0419/DRNe
Camera Lens Super-Resolution
Existing methods for single image super-resolution (SR) are typically
evaluated with synthetic degradation models such as bicubic or Gaussian
downsampling. In this paper, we investigate SR from the perspective of camera
lenses, named as CameraSR, which aims to alleviate the intrinsic tradeoff
between resolution (R) and field-of-view (V) in realistic imaging systems.
Specifically, we view the R-V degradation as a latent model in the SR process
and learn to reverse it with realistic low- and high-resolution image pairs. To
obtain the paired images, we propose two novel data acquisition strategies for
two representative imaging systems (i.e., DSLR and smartphone cameras),
respectively. Based on the obtained City100 dataset, we quantitatively analyze
the performance of commonly-used synthetic degradation models, and demonstrate
the superiority of CameraSR as a practical solution to boost the performance of
existing SR methods. Moreover, CameraSR can be readily generalized to different
content and devices, which serves as an advanced digital zoom tool in realistic
imaging systems. Codes and datasets are available at
https://github.com/ngchc/CameraSR.Comment: Accepted by CVPR 201
Multi-Scale Recursive and Perception-Distortion Controllable Image Super-Resolution
We describe our solution for the PIRM Super-Resolution Challenge 2018 where
we achieved the 2nd best perceptual quality for average RMSE<=16, 5th best for
RMSE<=12.5, and 7th best for RMSE<=11.5. We modify a recently proposed
Multi-Grid Back-Projection (MGBP) architecture to work as a generative system
with an input parameter that can control the amount of artificial details in
the output. We propose a discriminator for adversarial training with the
following novel properties: it is multi-scale that resembles a progressive-GAN;
it is recursive that balances the architecture of the generator; and it
includes a new layer to capture significant statistics of natural images.
Finally, we propose a training strategy that avoids conflicts between
reconstruction and perceptual losses. Our configuration uses only 281k
parameters and upscales each image of the competition in 0.2s in average.Comment: In ECCV 2018 Workshops. Won 2nd place in Region 3 of PIRM-SR
Challenge 2018. Code and models are available at
https://github.com/pnavarre/pirm-sr-201
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