72,785 research outputs found
Spatial-Adaptive Network for Single Image Denoising
Previous works have shown that convolutional neural networks can achieve good
performance in image denoising tasks. However, limited by the local rigid
convolutional operation, these methods lead to oversmoothing artifacts. A
deeper network structure could alleviate these problems, but more computational
overhead is needed. In this paper, we propose a novel spatial-adaptive
denoising network (SADNet) for efficient single image blind noise removal. To
adapt to changes in spatial textures and edges, we design a residual
spatial-adaptive block. Deformable convolution is introduced to sample the
spatially correlated features for weighting. An encoder-decoder structure with
a context block is introduced to capture multiscale information. With noise
removal from the coarse to fine, a high-quality noisefree image can be
obtained. We apply our method to both synthetic and real noisy image datasets.
The experimental results demonstrate that our method can surpass the
state-of-the-art denoising methods both quantitatively and visually
Sensitivity improvements for Shack-Hartmann wavefront sensors using total variation minimisation
We investigate the improvements in Shack-Hartmann wavefront sensor image
processing that can be realised using total variation minimisation techniques
to remove noise from these images. We perform Monte-Carlo simulation to
demonstrate that at certain signal-to-noise levels, sensitivity improvements of
up to one astronomical magnitude can be realised. We also present on-sky
measurements taken with the CANARY adaptive optics system that demonstrate an
improvement in performance when this technique is employed, and show that this
algorithm can be implemented in a real-time control system. We conclude that
total variation minimisation can lead to improvements in sensitivity of up to
one astronomical magnitude when used with adaptive optics systems.Comment: Accepted for publication in MNRAS. Second version has type fixed (now
-> not), 3rd version has corrected author lis
Variational based Mixed Noise Removal with CNN Deep Learning Regularization
In this paper, the traditional model based variational method and learning
based algorithms are naturally integrated to address mixed noise removal
problem. To be different from single type noise (e.g. Gaussian) removal, it is
a challenge problem to accurately discriminate noise types and levels for each
pixel. We propose a variational method to iteratively estimate the noise
parameters, and then the algorithm can automatically classify the noise
according to the different statistical parameters. The proposed variational
problem can be separated into regularization, synthesis, parameter estimation
and noise classification four steps with the operator splitting scheme. Each
step is related to an optimization subproblem. To enforce the regularization,
the deep learning method is employed to learn the natural images priori.
Compared with some model based regularizations, the CNN regularizer can
significantly improve the quality of the restored images. Compared with some
learning based methods, the synthesis step can produce better reconstructions
by analyzing the recognized noise types and levels. In our method, the
convolution neutral network (CNN) can be regarded as an operator which
associated to a variational functional. From this viewpoint, the proposed
method can be extended to many image reconstruction and inverse problems.
Numerical experiments in the paper show that our method can achieve some
state-of-the-art results for mixed noise removal
Fast and High Quality Highlight Removal from A Single Image
Specular reflection exists widely in photography and causes the recorded
color deviating from its true value, so fast and high quality highlight removal
from a single nature image is of great importance. In spite of the progress in
the past decades in highlight removal, achieving wide applicability to the
large diversity of nature scenes is quite challenging. To handle this problem,
we propose an analytic solution to highlight removal based on an L2
chromaticity definition and corresponding dichromatic model. Specifically, this
paper derives a normalized dichromatic model for the pixels with identical
diffuse color: a unit circle equation of projection coefficients in two
subspaces that are orthogonal to and parallel with the illumination,
respectively. In the former illumination orthogonal subspace, which is
specular-free, we can conduct robust clustering with an explicit criterion to
determine the cluster number adaptively. In the latter illumination parallel
subspace, a property called pure diffuse pixels distribution rule (PDDR) helps
map each specular-influenced pixel to its diffuse component. In terms of
efficiency, the proposed approach involves few complex calculation, and thus
can remove highlight from high resolution images fast. Experiments show that
this method is of superior performance in various challenging cases.Comment: 11 pages, 10 figures, submitted to IEEE TI
Adaptive Real-Time Removal of Impulse Noise in Medical Images
Noise is an important factor that degrades the quality of medical images.
Impulse noise is a common noise, which is caused by malfunctioning of sensor
elements or errors in the transmission of images. In medical images due to
presence of white foreground and black background, many pixels have intensities
similar to impulse noise and distinction between noisy and regular pixels is
difficult. In software techniques, the accuracy of the noise removal is more
important than the algorithm's complexity. But for hardware implementation
having a low complexity algorithm with an acceptable accuracy is essential. In
this paper a low complexity de-noising method is proposed that removes the
noise by local analysis of the image blocks. The proposed method distinguishes
non-noisy pixels that have noise-like intensities. All steps are designed to
have low hardware complexity. Simulation results show that for different
magnetic resonance images, the proposed method removes impulse noise with an
acceptable accuracy.Comment: 9 pages, 12 figures, 2 table
Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network
Because of the internal malfunction of satellite sensors and poor atmospheric
conditions such as thick cloud, the acquired remote sensing data often suffer
from missing information, i.e., the data usability is greatly reduced. In this
paper, a novel method of missing information reconstruction in remote sensing
images is proposed. The unified spatial-temporal-spectral framework based on a
deep convolutional neural network (STS-CNN) employs a unified deep
convolutional neural network combined with spatial-temporal-spectral
supplementary information. In addition, to address the fact that most methods
can only deal with a single missing information reconstruction task, the
proposed approach can solve three typical missing information reconstruction
tasks: 1) dead lines in Aqua MODIS band 6; 2) the Landsat ETM+ Scan Line
Corrector (SLC)-off problem; and 3) thick cloud removal. It should be noted
that the proposed model can use multi-source data (spatial, spectral, and
temporal) as the input of the unified framework. The results of both simulated
and real-data experiments demonstrate that the proposed model exhibits high
effectiveness in the three missing information reconstruction tasks listed
above.Comment: To be published in IEEE Transactions on Geoscience and Remote Sensin
External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising
Most of existing image denoising methods learn image priors from either
external data or the noisy image itself to remove noise. However, priors
learned from external data may not be adaptive to the image to be denoised,
while priors learned from the given noisy image may not be accurate due to the
interference of corrupted noise. Meanwhile, the noise in real-world noisy
images is very complex, which is hard to be described by simple distributions
such as Gaussian distribution, making real-world noisy image denoising a very
challenging problem. We propose to exploit the information in both external
data and the given noisy image, and develop an external prior guided internal
prior learning method for real-world noisy image denoising. We first learn
external priors from an independent set of clean natural images. With the aid
of learned external priors, we then learn internal priors from the given noisy
image to refine the prior model. The external and internal priors are
formulated as a set of orthogonal dictionaries to efficiently reconstruct the
desired image. Extensive experiments are performed on several real-world noisy
image datasets. The proposed method demonstrates highly competitive denoising
performance, outperforming state-of-the-art denoising methods including those
designed for real-world noisy images.Comment: 14 pages, 13figures, IEEE Trans. Image Processing 27(6): 2996-3010
(2018
CFSNet: Toward a Controllable Feature Space for Image Restoration
Deep learning methods have witnessed the great progress in image restoration
with specific metrics (e.g., PSNR, SSIM). However, the perceptual quality of
the restored image is relatively subjective, and it is necessary for users to
control the reconstruction result according to personal preferences or image
characteristics, which cannot be done using existing deterministic networks.
This motivates us to exquisitely design a unified interactive framework for
general image restoration tasks. Under this framework, users can control
continuous transition of different objectives, e.g., the perception-distortion
trade-off of image super-resolution, the trade-off between noise reduction and
detail preservation. We achieve this goal by controlling the latent features of
the designed network. To be specific, our proposed framework, named
Controllable Feature Space Network (CFSNet), is entangled by two branches based
on different objectives. Our framework can adaptively learn the coupling
coefficients of different layers and channels, which provides finer control of
the restored image quality. Experiments on several typical image restoration
tasks fully validate the effective benefits of the proposed method. Code is
available at https://github.com/qibao77/CFSNet.Comment: Accepted by ICCV 201
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration
Hyperspectral imaging, providing abundant spatial and spectral information
simultaneously, has attracted a lot of interest in recent years. Unfortunately,
due to the hardware limitations, the hyperspectral image (HSI) is vulnerable to
various degradations, such noises (random noise, HSI denoising), blurs
(Gaussian and uniform blur, HSI deblurring), and down-sampled (both spectral
and spatial downsample, HSI super-resolution). Previous HSI restoration methods
are designed for one specific task only. Besides, most of them start from the
1-D vector or 2-D matrix models and cannot fully exploit the structurally
spectral-spatial correlation in 3-D HSI. To overcome these limitations, in this
work, we propose a unified low-rank tensor recovery model for comprehensive HSI
restoration tasks, in which non-local similarity between spectral-spatial cubic
and spectral correlation are simultaneously captured by 3-order tensors.
Further, to improve the capability and flexibility, we formulate it as a
weighted low-rank tensor recovery (WLRTR) model by treating the singular values
differently, and study its analytical solution. We also consider the exclusive
stripe noise in HSI as the gross error by extending WLRTR to robust principal
component analysis (WLRTR-RPCA). Extensive experiments demonstrate the proposed
WLRTR models consistently outperform state-of-the-arts in typical low level
vision HSI tasks, including denoising, destriping, deblurring and
super-resolution.Comment: 22 pages, 22 figure
Phase asymmetry guided adaptive fractional-order total variation and diffusion for feature-preserving ultrasound despeckling
It is essential for ultrasound despeckling to remove speckle noise while
simultaneously preserving edge features for accurate diagnosis and analysis in
many applications. To preserve real edges such as ramp edges and low contrast
edges, we first detect edges using a phase-based measure called phase asymmetry
(PAS), which can distinguish small differences in transition border regions and
varies from to , taking in ideal smooth regions and taking at
ideal step edges. We further propose three strategies to properly preserve
edges. First, in observing that fractional-order anisotropic diffusion (FAD)
filter has good performance in smooth regions while the fractional-order TV
(FTV) filter performs better at edges, we leverage the PAS metric to keep a
balance between FAD filter and FTV filter for achieving the best performance of
preserving ramp edges. Second, considering that the FAD filter fails to protect
low contrast edges by solely integrating gradient information into the
diffusion coefficient, we integrate the PAS metric into the diffusion
coefficient to properly preserve low contrast edges. Finally, different from
fixed fractional order diffusion filters neglecting the differences between
smooth regions and transition border regions, an adaptive fractional order is
implemented based on the PAS metric to enhance edges. The experimental results
show that our method outperforms other state-of-the-art ultrasound despeckling
filters in both speckle reduction and feature preservation
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