11,638 research outputs found
Variational models for joint subsampling and reconstruction of turbulence-degraded images
Turbulence-degraded image frames are distorted by both turbulent deformations
and space-time-varying blurs. To suppress these effects, we propose a
multi-frame reconstruction scheme to recover a latent image from the observed
image sequence. Recent approaches are commonly based on registering each frame
to a reference image, by which geometric turbulent deformations can be
estimated and a sharp image can be restored. A major challenge is that a fine
reference image is usually unavailable, as every turbulence-degraded frame is
distorted. A high-quality reference image is crucial for the accurate
estimation of geometric deformations and fusion of frames. Besides, it is
unlikely that all frames from the image sequence are useful, and thus frame
selection is necessary and highly beneficial. In this work, we propose a
variational model for joint subsampling of frames and extraction of a clear
image. A fine image and a suitable choice of subsample are simultaneously
obtained by iteratively reducing an energy functional. The energy consists of a
fidelity term measuring the discrepancy between the extracted image and the
subsampled frames, as well as regularization terms on the extracted image and
the subsample. Different choices of fidelity and regularization terms are
explored. By carefully selecting suitable frames and extracting the image, the
quality of the reconstructed image can be significantly improved. Extensive
experiments have been carried out, which demonstrate the efficacy of our
proposed model. In addition, the extracted subsamples and images can be put in
existing algorithms to produce improved results.Comment: arXiv admin note: text overlap with arXiv:1704.0314
Restoration of Atmospheric Turbulence-distorted Images via RPCA and Quasiconformal Maps
We address the problem of restoring a high-quality image from an observed
image sequence strongly distorted by atmospheric turbulence. A novel algorithm
is proposed in this paper to reduce geometric distortion as well as
space-and-time-varying blur due to strong turbulence. By considering a suitable
energy functional, our algorithm first obtains a sharp reference image and a
subsampled image sequence containing sharp and mildly distorted image frames
with respect to the reference image. The subsampled image sequence is then
stabilized by applying the Robust Principal Component Analysis (RPCA) on the
deformation fields between image frames and warping the image frames by a
quasiconformal map associated with the low-rank part of the deformation matrix.
After image frames are registered to the reference image, the low-rank part of
them are deblurred via a blind deconvolution, and the deblurred frames are then
fused with the enhanced sparse part. Experiments have been carried out on both
synthetic and real turbulence-distorted video. Results demonstrate that our
method is effective in alleviating distortions and blur, restoring image
details and enhancing visual quality.Comment: 21 pages, 24 figure
Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding
Multishot Magnetic Resonance Imaging (MRI) is a promising imaging modality
that can produce a high-resolution image with relatively less data acquisition
time. The downside of multishot MRI is that it is very sensitive to subject
motion and even small amounts of motion during the scan can produce artifacts
in the final MR image that may cause misdiagnosis. Numerous efforts have been
made to address this issue; however, all of these proposals are limited in
terms of how much motion they can correct and the required computational time.
In this paper, we propose a novel generative networks based conjugate gradient
SENSE (CG-SENSE) reconstruction framework for motion correction in multishot
MRI. The proposed framework first employs CG-SENSE reconstruction to produce
the motion-corrupted image and then a generative adversarial network (GAN) is
used to correct the motion artifacts. The proposed method has been rigorously
evaluated on synthetically corrupted data on varying degrees of motion, numbers
of shots, and encoding trajectories. Our analyses (both quantitative as well as
qualitative/visual analysis) establishes that the proposed method significantly
robust and outperforms state-of-the-art motion correction techniques and also
reduces severalfold of computational times.Comment: This paper has been published in Scientific Reports Journa
CNN based dense underwater 3D scene reconstruction by transfer learning using bubble database
Dense 3D shape acquisition of swimming human or live fish is an important
research topic for sports, biological science and so on. For this purpose,
active stereo sensor is usually used in the air, however it cannot be applied
to the underwater environment because of refraction, strong light attenuation
and severe interference of bubbles. Passive stereo is a simple solution for
capturing dynamic scenes at underwater environment, however the shape with
textureless surfaces or irregular reflections cannot be recovered. Recently,
the stereo camera pair with a pattern projector for adding artificial textures
on the objects is proposed. However, to use the system for underwater
environment, several problems should be compensated, i.e., disturbance by
fluctuation and bubbles. Simple solution is to use convolutional neural network
for stereo to cancel the effects of bubbles and/or water fluctuation. Since it
is not easy to train CNN with small size of database with large variation, we
develop a special bubble generation device to efficiently create real bubble
database of multiple size and density. In addition, we propose a transfer
learning technique for multi-scale CNN to effectively remove bubbles and
projected-patterns on the object. Further, we develop a real system and
actually captured live swimming human, which has not been done before.
Experiments are conducted to show the effectiveness of our method compared with
the state of the art techniques.Comment: IEEE Winter Conference on Applications of Computer Vision. arXiv
admin note: text overlap with arXiv:1808.0834
A New Adaptive Video Super-Resolution Algorithm With Improved Robustness to Innovations
In this paper, a new video super-resolution reconstruction (SRR) method with
improved robustness to outliers is proposed. Although the R-LMS is one of the
SRR algorithms with the best reconstruction quality for its computational cost,
and is naturally robust to registration inaccuracies, its performance is known
to degrade severely in the presence of innovation outliers. By studying the
proximal point cost function representation of the R-LMS iterative equation, a
better understanding of its performance under different situations is attained.
Using statistical properties of typical innovation outliers, a new cost
function is then proposed and two new algorithms are derived, which present
improved robustness to outliers while maintaining computational costs
comparable to that of R-LMS. Monte Carlo simulation results illustrate that the
proposed method outperforms the traditional and regularized versions of LMS,
and is competitive with state-of-the-art SRR methods at a much smaller
computational cost
Optimized Pre-Compensating Compression
In imaging systems, following acquisition, an image/video is transmitted or
stored and eventually presented to human observers using different and often
imperfect display devices. While the resulting quality of the output image may
severely be affected by the display, this degradation is usually ignored in the
preceding compression. In this paper we model the sub-optimality of the display
device as a known degradation operator applied on the decompressed image/video.
We assume the use of a standard compression path, and augment it with a
suitable pre-processing procedure, providing a compressed signal intended to
compensate the degradation without any post-filtering. Our approach originates
from an intricate rate-distortion problem, optimizing the modifications to the
input image/video for reaching best end-to-end performance. We address this
seemingly computationally intractable problem using the alternating direction
method of multipliers (ADMM) approach, leading to a procedure in which a
standard compression technique is iteratively applied. We demonstrate the
proposed method for adjusting HEVC image/video compression to compensate
post-decompression visual effects due to a common type of displays.
Particularly, we use our method to reduce motion-blur perceived while viewing
video on LCD devices. The experiments establish our method as a leading
approach for preprocessing high bit-rate compression to counterbalance a
post-decompression degradation
Distortion-driven Turbulence Effect Removal using Variational Model
It remains a challenge to simultaneously remove geometric distortion and
space-time-varying blur in frames captured through a turbulent atmospheric
medium. To solve, or at least reduce these effects, we propose a new scheme to
recover a latent image from observed frames by integrating a new variational
model and distortion-driven spatial-temporal kernel regression. The proposed
scheme first constructs a high-quality reference image from the observed frames
using low-rank decomposition. Then, to generate an improved registered
sequence, the reference image is iteratively optimized using a variational
model containing a new spatial-temporal regularization. The proposed fast
algorithm efficiently solves this model without the use of partial differential
equations (PDEs). Next, to reduce blur variation, distortion-driven
spatial-temporal kernel regression is carried out to fuse the registered
sequence into one image by introducing the concept of the near-stationary
patch. Applying a blind deconvolution algorithm to the fused image produces the
final output. Extensive experimental testing shows, both qualitatively and
quantitatively, that the proposed method can effectively alleviate distortion
and blur and recover details of the original scene compared to state-of-the-art
methods.Comment: 28 pages, 15 figure
3D Surface Reconstruction of Underwater Objects
In this paper, we propose a novel technique to reconstruct 3D surface of an
underwater object using stereo images. Reconstructing the 3D surface of an
underwater object is really a challenging task due to degraded quality of
underwater images. There are various reason of quality degradation of
underwater images i.e., non-uniform illumination of light on the surface of
objects, scattering and absorption effects. Floating particles present in
underwater produces Gaussian noise on the captured underwater images which
degrades the quality of images. The degraded underwater images are preprocessed
by applying homomorphic, wavelet denoising and anisotropic filtering
sequentially. The uncalibrated rectification technique is applied to
preprocessed images to rectify the left and right images. The rectified left
and right image lies on a common plane. To find the correspondence points in a
left and right images, we have applied dense stereo matching technique i.e.,
graph cut method. Finally, we estimate the depth of images using triangulation
technique. The experimental result shows that the proposed method reconstruct
3D surface of underwater objects accurately using captured underwater stereo
images.Comment: International Journal of Computer Applications (2012
Bringing Alive Blurred Moments
We present a solution for the goal of extracting a video from a single motion
blurred image to sequentially reconstruct the clear views of a scene as beheld
by the camera during the time of exposure. We first learn motion representation
from sharp videos in an unsupervised manner through training of a convolutional
recurrent video autoencoder network that performs a surrogate task of video
reconstruction. Once trained, it is employed for guided training of a motion
encoder for blurred images. This network extracts embedded motion information
from the blurred image to generate a sharp video in conjunction with the
trained recurrent video decoder. As an intermediate step, we also design an
efficient architecture that enables real-time single image deblurring and
outperforms competing methods across all factors: accuracy, speed, and
compactness. Experiments on real scenes and standard datasets demonstrate the
superiority of our framework over the state-of-the-art and its ability to
generate a plausible sequence of temporally consistent sharp frames.Comment: CVPR 201
MARLow: A Joint Multiplanar Autoregressive and Low-Rank Approach for Image Completion
In this paper, we propose a novel multiplanar autoregressive (AR) model to
exploit the correlation in cross-dimensional planes of a similar patch group
collected in an image, which has long been neglected by previous AR models. On
that basis, we then present a joint multiplanar AR and low-rank based approach
(MARLow) for image completion from random sampling, which exploits the nonlocal
self-similarity within natural images more effectively. Specifically, the
multiplanar AR model constraints the local stationarity in different
cross-sections of the patch group, while the low-rank minimization captures the
intrinsic coherence of nonlocal patches. The proposed approach can be readily
extended to multichannel images (e.g. color images), by simultaneously
considering the correlation in different channels. Experimental results
demonstrate that the proposed approach significantly outperforms
state-of-the-art methods, even if the pixel missing rate is as high as 90%.Comment: 16 pages, 9 figure
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