28,122 research outputs found
NTIRE 2020 Challenge on Image Demoireing: Methods and Results
This paper reviews the Challenge on Image Demoireing that was part of the New
Trends in Image Restoration and Enhancement (NTIRE) workshop, held in
conjunction with CVPR 2020. Demoireing is a difficult task of removing moire
patterns from an image to reveal an underlying clean image. The challenge was
divided into two tracks. Track 1 targeted the single image demoireing problem,
which seeks to remove moire patterns from a single image. Track 2 focused on
the burst demoireing problem, where a set of degraded moire images of the same
scene were provided as input, with the goal of producing a single demoired
image as output. The methods were ranked in terms of their fidelity, measured
using the peak signal-to-noise ratio (PSNR) between the ground truth clean
images and the restored images produced by the participants' methods. The
tracks had 142 and 99 registered participants, respectively, with a total of 14
and 6 submissions in the final testing stage. The entries span the current
state-of-the-art in image and burst image demoireing problems
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
Triple Attention Mixed Link Network for Single Image Super Resolution
Single image super resolution is of great importance as a low-level computer
vision task. Recent approaches with deep convolutional neural networks have
achieved im-pressive performance. However, existing architectures have
limitations due to the less sophisticated structure along with less strong
representational power. In this work, to significantly enhance the feature
representation, we proposed Triple Attention mixed link Network (TAN) which
consists of 1) three different aspects (i.e., kernel, spatial and channel) of
attention mechanisms and 2) fu-sion of both powerful residual and dense
connections (i.e., mixed link). Specifically, the network with multi kernel
learns multi hierarchical representations under different receptive fields. The
output features are recalibrated by the effective kernel and channel attentions
and feed into next layer partly residual and partly dense, which filters the
information and enable the network to learn more powerful representations. The
features finally pass through the spatial attention in the reconstruction
network which generates a fusion of local and global information, let the
network restore more details and improves the quality of reconstructed images.
Thanks to the diverse feature recalibrations and the advanced information flow
topology, our proposed model is strong enough to per-form against the
state-of-the-art methods on the bench-mark evaluations
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution
Convolutional neural networks (CNNs) have demonstrated superior performance
in super-resolution (SR). However, most CNN-based SR methods neglect the
different importance among feature channels or fail to take full advantage of
the hierarchical features. To address these issues, this paper presents a novel
recursive unit. Firstly, at the beginning of each unit, we adopt a compact
channel attention mechanism to adaptively recalibrate the channel importance of
input features. Then, the multi-level features, rather than only deep-level
features, are extracted and fused. Additionally, we find that it will force our
model to learn more details by using the learnable upsampling method (i.e.,
transposed convolution) only on residual branch (instead of using it both on
residual branch and identity branch) while using the bicubic interpolation on
the other branch. Analytic experiments show that our method achieves
competitive results compared with the state-of-the-art methods and maintains
faster speed as well.Comment: 4 pages, 3 figures, Accepted as an oral presentation at VCI
A Matrix-in-matrix Neural Network for Image Super Resolution
In recent years, deep learning methods have achieved impressive results with
higher peak signal-to-noise ratio in single image super-resolution (SISR) tasks
by utilizing deeper layers. However, their application is quite limited since
they require high computing power. In addition, most of the existing methods
rarely take full advantage of the intermediate features which are helpful for
restoration. To address these issues, we propose a moderate-size SISR net work
named matrixed channel attention network (MCAN) by constructing a matrix
ensemble of multi-connected channel attention blocks (MCAB). Several models of
different sizes are released to meet various practical requirements.
Conclusions can be drawn from our extensive benchmark experiments that the
proposed models achieve better performance with much fewer multiply-adds and
parameters. Our models will be made publicly available
NTIRE 2020 Challenge on Image and Video Deblurring
Motion blur is one of the most common degradation artifacts in dynamic scene
photography. This paper reviews the NTIRE 2020 Challenge on Image and Video
Deblurring. In this challenge, we present the evaluation results from 3
competition tracks as well as the proposed solutions. Track 1 aims to develop
single-image deblurring methods focusing on restoration quality. On Track 2,
the image deblurring methods are executed on a mobile platform to find the
balance of the running speed and the restoration accuracy. Track 3 targets
developing video deblurring methods that exploit the temporal relation between
input frames. In each competition, there were 163, 135, and 102 registered
participants and in the final testing phase, 9, 4, and 7 teams competed. The
winning methods demonstrate the state-ofthe-art performance on image and video
deblurring tasks.Comment: To be published in CVPR 2020 Workshop (New Trends in Image
Restoration and Enhancement
NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image
This paper reviews the second challenge on spectral reconstruction from RGB
images, i.e., the recovery of whole-scene hyperspectral (HS) information from a
3-channel RGB image. As in the previous challenge, two tracks were provided:
(i) a "Clean" track where HS images are estimated from noise-free RGBs, the RGB
images are themselves calculated numerically using the ground-truth HS images
and supplied spectral sensitivity functions (ii) a "Real World" track,
simulating capture by an uncalibrated and unknown camera, where the HS images
are recovered from noisy JPEG-compressed RGB images. A new, larger-than-ever,
natural hyperspectral image data set is presented, containing a total of 510 HS
images. The Clean and Real World tracks had 103 and 78 registered participants
respectively, with 14 teams competing in the final testing phase. A description
of the proposed methods, alongside their challenge scores and an extensive
evaluation of top performing methods is also provided. They gauge the
state-of-the-art in spectral reconstruction from an RGB image
Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery
Recently, single gray/RGB image super-resolution reconstruction task has been
extensively studied and made significant progress by leveraging the advanced
machine learning techniques based on deep convolutional neural networks
(DCNNs). However, there has been limited technical development focusing on
single hyperspectral image super-resolution due to the high-dimensional and
complex spectral patterns in hyperspectral image. In this paper, we make a step
forward by investigating how to adapt state-of-the-art residual learning based
single gray/RGB image super-resolution approaches for computationally efficient
single hyperspectral image super-resolution, referred as SSPSR. Specifically,
we introduce a spatial-spectral prior network (SSPN) to fully exploit the
spatial information and the correlation between the spectra of the
hyperspectral data. Considering that the hyperspectral training samples are
scarce and the spectral dimension of hyperspectral image data is very high, it
is nontrivial to train a stable and effective deep network. Therefore, a group
convolution (with shared network parameters) and progressive upsampling
framework is proposed. This will not only alleviate the difficulty in feature
extraction due to high-dimension of the hyperspectral data, but also make the
training process more stable. To exploit the spatial and spectral prior, we
design a spatial-spectral block (SSB), which consists of a spatial residual
module and a spectral attention residual module. Experimental results on some
hyperspectral images demonstrate that the proposed SSPSR method enhances the
details of the recovered high-resolution hyperspectral images, and outperforms
state-of-the-arts. The source code is available at
\url{https://github.com/junjun-jiang/SSPSRComment: Accepted for publication at IEEE Transactions on Computational
Imagin
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
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
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