218 research outputs found
Real Image Denoising with Feature Attention
Deep convolutional neural networks perform better on images containing
spatially invariant noise (synthetic noise); however, their performance is
limited on real-noisy photographs and requires multiple stage network modeling.
To advance the practicability of denoising algorithms, this paper proposes a
novel single-stage blind real image denoising network (RIDNet) by employing a
modular architecture. We use a residual on the residual structure to ease the
flow of low-frequency information and apply feature attention to exploit the
channel dependencies. Furthermore, the evaluation in terms of quantitative
metrics and visual quality on three synthetic and four real noisy datasets
against 19 state-of-the-art algorithms demonstrate the superiority of our
RIDNet.Comment: Accepted in ICCV (Oral), 201
Comparison of Image Quality Models for Optimization of Image Processing Systems
The performance of objective image quality assessment (IQA) models has been
evaluated primarily by comparing model predictions to human quality judgments.
Perceptual datasets gathered for this purpose have provided useful benchmarks
for improving IQA methods, but their heavy use creates a risk of overfitting.
Here, we perform a large-scale comparison of IQA models in terms of their use
as objectives for the optimization of image processing algorithms.
Specifically, we use eleven full-reference IQA models to train deep neural
networks for four low-level vision tasks: denoising, deblurring,
super-resolution, and compression. Subjective testing on the optimized images
allows us to rank the competing models in terms of their perceptual
performance, elucidate their relative advantages and disadvantages in these
tasks, and propose a set of desirable properties for incorporation into future
IQA models
200x Low-dose PET Reconstruction using Deep Learning
Positron emission tomography (PET) is widely used in various clinical
applications, including cancer diagnosis, heart disease and neuro disorders.
The use of radioactive tracer in PET imaging raises concerns due to the risk of
radiation exposure. To minimize this potential risk in PET imaging, efforts
have been made to reduce the amount of radio-tracer usage. However, lowing dose
results in low Signal-to-Noise-Ratio (SNR) and loss of information, both of
which will heavily affect clinical diagnosis. Besides, the ill-conditioning of
low-dose PET image reconstruction makes it a difficult problem for iterative
reconstruction algorithms. Previous methods proposed are typically complicated
and slow, yet still cannot yield satisfactory results at significantly low
dose. Here, we propose a deep learning method to resolve this issue with an
encoder-decoder residual deep network with concatenate skip connections.
Experiments shows the proposed method can reconstruct low-dose PET image to a
standard-dose quality with only two-hundredth dose. Different cost functions
for training model are explored. Multi-slice input strategy is introduced to
provide the network with more structural information and make it more robust to
noise. Evaluation on ultra-low-dose clinical data shows that the proposed
method can achieve better result than the state-of-the-art methods and
reconstruct images with comparable quality using only 0.5% of the original
regular dose
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
Magnetic resonance image (MRI) reconstruction is a severely ill-posed linear
inverse task demanding time and resource intensive computations that can
substantially trade off {\it accuracy} for {\it speed} in real-time imaging. In
addition, state-of-the-art compressed sensing (CS) analytics are not cognizant
of the image {\it diagnostic quality}. To cope with these challenges we put
forth a novel CS framework that permeates benefits from generative adversarial
networks (GAN) to train a (low-dimensional) manifold of diagnostic-quality MR
images from historical patients. Leveraging a mixture of least-squares (LS)
GANs and pixel-wise cost, a deep residual network with skip
connections is trained as the generator that learns to remove the {\it
aliasing} artifacts by projecting onto the manifold. LSGAN learns the texture
details, while controls the high-frequency noise. A multilayer
convolutional neural network is then jointly trained based on diagnostic
quality images to discriminate the projection quality. The test phase performs
feed-forward propagation over the generator network that demands a very low
computational overhead. Extensive evaluations are performed on a large
contrast-enhanced MR dataset of pediatric patients. In particular, images rated
based on expert radiologists corroborate that GANCS retrieves high contrast
images with detailed texture relative to conventional CS, and pixel-wise
schemes. In addition, it offers reconstruction under a few milliseconds, two
orders of magnitude faster than state-of-the-art CS-MRI schemes
Pyramid Attention Networks for Image Restoration
Self-similarity refers to the image prior widely used in image restoration
algorithms that small but similar patterns tend to occur at different locations
and scales. However, recent advanced deep convolutional neural network based
methods for image restoration do not take full advantage of self-similarities
by relying on self-attention neural modules that only process information at
the same scale. To solve this problem, we present a novel Pyramid Attention
module for image restoration, which captures long-range feature correspondences
from a multi-scale feature pyramid. Inspired by the fact that corruptions, such
as noise or compression artifacts, drop drastically at coarser image scales,
our attention module is designed to be able to borrow clean signals from their
"clean" correspondences at the coarser levels. The proposed pyramid attention
module is a generic building block that can be flexibly integrated into various
neural architectures. Its effectiveness is validated through extensive
experiments on multiple image restoration tasks: image denoising, demosaicing,
compression artifact reduction, and super resolution. Without any bells and
whistles, our PANet (pyramid attention module with simple network backbones)
can produce state-of-the-art results with superior accuracy and visual quality.
Our code will be available at
https://github.com/SHI-Labs/Pyramid-Attention-Network
Zero-order Reverse Filtering
In this paper, we study an unconventional but practically meaningful
reversibility problem of commonly used image filters. We broadly define filters
as operations to smooth images or to produce layers via global or local
algorithms. And we raise the intriguingly problem if they are reservable to the
status before filtering. To answer it, we present a novel strategy to
understand general filter via contraction mappings on a metric space. A very
simple yet effective zero-order algorithm is proposed. It is able to
practically reverse most filters with low computational cost. We present quite
a few experiments in the paper and supplementary file to thoroughly verify its
performance. This method can also be generalized to solve other inverse
problems and enables new applications.Comment: 9 pages, submitted to conferenc
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
Image restoration is a long-standing problem in low-level computer vision
with many interesting applications. We describe a flexible learning framework
based on the concept of nonlinear reaction diffusion models for various image
restoration problems. By embodying recent improvements in nonlinear diffusion
models, we propose a dynamic nonlinear reaction diffusion model with
time-dependent parameters (\ie, linear filters and influence functions). In
contrast to previous nonlinear diffusion models, all the parameters, including
the filters and the influence functions, are simultaneously learned from
training data through a loss based approach. We call this approach TNRD --
\textit{Trainable Nonlinear Reaction Diffusion}. The TNRD approach is
applicable for a variety of image restoration tasks by incorporating
appropriate reaction force. We demonstrate its capabilities with three
representative applications, Gaussian image denoising, single image super
resolution and JPEG deblocking. Experiments show that our trained nonlinear
diffusion models largely benefit from the training of the parameters and
finally lead to the best reported performance on common test datasets for the
tested applications. Our trained models preserve the structural simplicity of
diffusion models and take only a small number of diffusion steps, thus are
highly efficient. Moreover, they are also well-suited for parallel computation
on GPUs, which makes the inference procedure extremely fast.Comment: 14 pages, 13 figures, to appear in IEEE Transactions on Pattern
Analysis and Machine Intelligence (TPAMI
Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss
In this paper, we introduce a new CT image denoising method based on the
generative adversarial network (GAN) with Wasserstein distance and perceptual
similarity. The Wasserstein distance is a key concept of the optimal transform
theory, and promises to improve the performance of the GAN. The perceptual loss
compares the perceptual features of a denoised output against those of the
ground truth in an established feature space, while the GAN helps migrate the
data noise distribution from strong to weak. Therefore, our proposed method
transfers our knowledge of visual perception to the image denoising task, is
capable of not only reducing the image noise level but also keeping the
critical information at the same time. Promising results have been obtained in
our experiments with clinical CT images
Near-lossless -constrained Image Decompression via Deep Neural Network
Recently a number of CNN-based techniques were proposed to remove image
compression artifacts. As in other restoration applications, these techniques
all learn a mapping from decompressed patches to the original counterparts
under the ubiquitous metric. However, this approach is incapable
of restoring distinctive image details which may be statistical outliers but
have high semantic importance (e.g., tiny lesions in medical images). To
overcome this weakness, we propose to incorporate an fidelity
criterion in the design of neural network so that no small, distinctive
structures of the original image can be dropped or distorted. Experimental
results demonstrate that the proposed method outperforms the state-of-the-art
methods in error metric and perceptual quality, while being
competitive in error metric as well. It can restore subtle image
details that are otherwise destroyed or missed by other algorithms. Our
research suggests a new machine learning paradigm of ultra high fidelity image
compression that is ideally suited for applications in medicine, space, and
sciences.Comment: Accepted by DCC 201
Towards Real-World Blind Face Restoration with Generative Facial Prior
Blind face restoration usually relies on facial priors, such as facial
geometry prior or reference prior, to restore realistic and faithful details.
However, very low-quality inputs cannot offer accurate geometric prior while
high-quality references are inaccessible, limiting the applicability in
real-world scenarios. In this work, we propose GFP-GAN that leverages rich and
diverse priors encapsulated in a pretrained face GAN for blind face
restoration. This Generative Facial Prior (GFP) is incorporated into the face
restoration process via novel channel-split spatial feature transform layers,
which allow our method to achieve a good balance of realness and fidelity.
Thanks to the powerful generative facial prior and delicate designs, our
GFP-GAN could jointly restore facial details and enhance colors with just a
single forward pass, while GAN inversion methods require expensive
image-specific optimization at inference. Extensive experiments show that our
method achieves superior performance to prior art on both synthetic and
real-world datasets.Comment: CVPR 2021. Codes: https://github.com/TencentARC/GFPGA
- β¦