3,135 research outputs found
Learning Dual Convolutional Neural Networks for Low-Level Vision
In this paper, we propose a general dual convolutional neural network
(DualCNN) for low-level vision problems, e.g., super-resolution,
edge-preserving filtering, deraining and dehazing. These problems usually
involve the estimation of two components of the target signals: structures and
details. Motivated by this, our proposed DualCNN consists of two parallel
branches, which respectively recovers the structures and details in an
end-to-end manner. The recovered structures and details can generate the target
signals according to the formation model for each particular application. The
DualCNN is a flexible framework for low-level vision tasks and can be easily
incorporated into existing CNNs. Experimental results show that the DualCNN can
be effectively applied to numerous low-level vision tasks with favorable
performance against the state-of-the-art methods.Comment: CVPR 201
Towards Real Scene Super-Resolution with Raw Images
Most existing super-resolution methods do not perform well in real scenarios
due to lack of realistic training data and information loss of the model input.
To solve the first problem, we propose a new pipeline to generate realistic
training data by simulating the imaging process of digital cameras. And to
remedy the information loss of the input, we develop a dual convolutional
neural network to exploit the originally captured radiance information in raw
images. In addition, we propose to learn a spatially-variant color
transformation which helps more effective color corrections. Extensive
experiments demonstrate that super-resolution with raw data helps recover fine
details and clear structures, and more importantly, the proposed network and
data generation pipeline achieve superior results for single image
super-resolution in real scenarios.Comment: Accepted in CVPR 2019, project page:
https://sites.google.com/view/xiangyuxu/rawsr_cvpr1
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
Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction
Several dual-domain convolutional neural network-based methods show
outstanding performance in reducing image compression artifacts. However, they
suffer from handling color images because the compression processes for
gray-scale and color images are completely different. Moreover, these methods
train a specific model for each compression quality and require multiple models
to achieve different compression qualities. To address these problems, we
proposed an implicit dual-domain convolutional network (IDCN) with the pixel
position labeling map and the quantization tables as inputs. Specifically, we
proposed an extractor-corrector framework-based dual-domain correction unit
(DCU) as the basic component to formulate the IDCN. A dense block was
introduced to improve the performance of extractor in DRU. The implicit
dual-domain translation allows the IDCN to handle color images with the
discrete cosine transform (DCT)-domain priors. A flexible version of IDCN
(IDCN-f) was developed to handle a wide range of compression qualities.
Experiments for both objective and subjective evaluations on benchmark datasets
show that IDCN is superior to the state-of-the-art methods and IDCN-f exhibits
excellent abilities to handle a wide range of compression qualities with little
performance sacrifice and demonstrates great potential for practical
applications.Comment: accepted by IEEE Transactions on Circuits and Systems for Video
Technology(T-CSVT
DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images
JPEG is one of the widely used lossy compression methods. JPEG-compressed
images usually suffer from compression artifacts including blocking and
blurring, especially at low bit-rates. Soft decoding is an effective solution
to improve the quality of compressed images without changing codec or
introducing extra coding bits. Inspired by the excellent performance of the
deep convolutional neural networks (CNNs) on both low-level and high-level
computer vision problems, we develop a dual pixel-wavelet domain deep
CNNs-based soft decoding network for JPEG-compressed images, namely DPW-SDNet.
The pixel domain deep network takes the four downsampled versions of the
compressed image to form a 4-channel input and outputs a pixel domain
prediction, while the wavelet domain deep network uses the 1-level discrete
wavelet transformation (DWT) coefficients to form a 4-channel input to produce
a DWT domain prediction. The pixel domain and wavelet domain estimates are
combined to generate the final soft decoded result. Experimental results
demonstrate the superiority of the proposed DPW-SDNet over several
state-of-the-art compression artifacts reduction algorithms.Comment: CVPRW 201
DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion
In this paper we propose a convolutional neural network that is designed to
upsample a series of sparse range measurements based on the contextual cues
gleaned from a high resolution intensity image. Our approach draws inspiration
from related work on super-resolution and in-painting. We propose a novel
architecture that seeks to pull contextual cues separately from the intensity
image and the depth features and then fuse them later in the network. We argue
that this approach effectively exploits the relationship between the two
modalities and produces accurate results while respecting salient image
structures. We present experimental results to demonstrate that our approach is
comparable with state of the art methods and generalizes well across multiple
datasets.Comment: 8 page
Framing U-Net via Deep Convolutional Framelets: Application to Sparse-view CT
X-ray computed tomography (CT) using sparse projection views is a recent
approach to reduce the radiation dose. However, due to the insufficient
projection views, an analytic reconstruction approach using the filtered back
projection (FBP) produces severe streaking artifacts. Recently, deep learning
approaches using large receptive field neural networks such as U-Net have
demonstrated impressive performance for sparse- view CT reconstruction.
However, theoretical justification is still lacking. Inspired by the recent
theory of deep convolutional framelets, the main goal of this paper is,
therefore, to reveal the limitation of U-Net and propose new multi-resolution
deep learning schemes. In particular, we show that the alternative U- Net
variants such as dual frame and the tight frame U-Nets satisfy the so-called
frame condition which make them better for effective recovery of high frequency
edges in sparse view- CT. Using extensive experiments with real patient data
set, we demonstrate that the new network architectures provide better
reconstruction performance.Comment: This will appear in IEEE Transaction on Medical Imaging, a special
issue of Machine Learning for Image Reconstructio
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
S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction
Recent studies have used deep residual convolutional neural networks (CNNs)
for JPEG compression artifact reduction. This study proposes a scalable CNN
called S-Net. Our approach effectively adjusts the network scale dynamically in
a multitask system for real-time operation with little performance loss. It
offers a simple and direct technique to evaluate the performance gains obtained
with increasing network depth, and it is helpful for removing redundant network
layers to maximize the network efficiency. We implement our architecture using
the Keras framework with the TensorFlow backend on an NVIDIA K80 GPU server. We
train our models on the DIV2K dataset and evaluate their performance on public
benchmark datasets. To validate the generality and universality of the proposed
method, we created and utilized a new dataset, called WIN143, for
over-processed images evaluation. Experimental results indicate that our
proposed approach outperforms other CNN-based methods and achieves
state-of-the-art performance.Comment: accepted by Journal of Electronic Imagin
MemNet: A Persistent Memory Network for Image Restoration
Recently, very deep convolutional neural networks (CNNs) have been attracting
considerable attention in image restoration. However, as the depth grows, the
long-term dependency problem is rarely realized for these very deep models,
which results in the prior states/layers having little influence on the
subsequent ones. Motivated by the fact that human thoughts have persistency, we
propose a very deep persistent memory network (MemNet) that introduces a memory
block, consisting of a recursive unit and a gate unit, to explicitly mine
persistent memory through an adaptive learning process. The recursive unit
learns multi-level representations of the current state under different
receptive fields. The representations and the outputs from the previous memory
blocks are concatenated and sent to the gate unit, which adaptively controls
how much of the previous states should be reserved, and decides how much of the
current state should be stored. We apply MemNet to three image restoration
tasks, i.e., image denosing, super-resolution and JPEG deblocking.
Comprehensive experiments demonstrate the necessity of the MemNet and its
unanimous superiority on all three tasks over the state of the arts. Code is
available at https://github.com/tyshiwo/MemNet.Comment: Accepted by ICCV 2017 (Spotlight presentation
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