2,916 research outputs found
Image Restoration from Patch-based Compressed Sensing Measurement
A series of methods have been proposed to reconstruct an image from
compressively sensed random measurement, but most of them have high time
complexity and are inappropriate for patch-based compressed sensing capture,
because of their serious blocky artifacts in the restoration results. In this
paper, we present a non-iterative image reconstruction method from patch-based
compressively sensed random measurement. Our method features two cascaded
networks based on residual convolution neural network to learn the end-to-end
full image restoration, which is capable of reconstructing image patches and
removing the blocky effect with low time cost. Experimental results on
synthetic and real data show that our method outperforms state-of-the-art
compressive sensing (CS) reconstruction methods with patch-based CS
measurement. To demonstrate the effectiveness of our method in more general
setting, we apply the de-block process in our method to JPEG compression
artifacts removal and achieve outstanding performance as well
The Power of Complementary Regularizers: Image Recovery via Transform Learning and Low-Rank Modeling
Recent works on adaptive sparse and on low-rank signal modeling have
demonstrated their usefulness in various image / video processing applications.
Patch-based methods exploit local patch sparsity, whereas other works apply
low-rankness of grouped patches to exploit image non-local structures. However,
using either approach alone usually limits performance in image reconstruction
or recovery applications. In this work, we propose a simultaneous sparsity and
low-rank model, dubbed STROLLR, to better represent natural images. In order to
fully utilize both the local and non-local image properties, we develop an
image restoration framework using a transform learning scheme with joint
low-rank regularization. The approach owes some of its computational efficiency
and good performance to the use of transform learning for adaptive sparse
representation rather than the popular synthesis dictionary learning
algorithms, which involve approximation of NP-hard sparse coding and expensive
learning steps. We demonstrate the proposed framework in various applications
to image denoising, inpainting, and compressed sensing based magnetic resonance
imaging. Results show promising performance compared to state-of-the-art
competing methods.Comment: 13 pages, 7 figures, submitted to TI
Nonlocal Low-Rank Tensor Factor Analysis for Image Restoration
Low-rank signal modeling has been widely leveraged to capture non-local
correlation in image processing applications. We propose a new method that
employs low-rank tensor factor analysis for tensors generated by grouped image
patches. The low-rank tensors are fed into the alternative direction multiplier
method (ADMM) to further improve image reconstruction. The motivating
application is compressive sensing (CS), and a deep convolutional architecture
is adopted to approximate the expensive matrix inversion in CS applications. An
iterative algorithm based on this low-rank tensor factorization strategy,
called NLR-TFA, is presented in detail. Experimental results on noiseless and
noisy CS measurements demonstrate the superiority of the proposed approach,
especially at low CS sampling rates
Joint group and residual sparse coding for image compressive sensing
Nonlocal self-similarity and group sparsity have been widely utilized in
image compressive sensing (CS). However, when the sampling rate is low, the
internal prior information of degraded images may be not enough for accurate
restoration, resulting in loss of image edges and details. In this paper, we
propose a joint group and residual sparse coding method for CS image recovery
(JGRSC-CS). In the proposed JGRSC-CS, patch group is treated as the basic unit
of sparse coding and two dictionaries (namely internal and external
dictionaries) are applied to exploit the sparse representation of each group
simultaneously. The internal self-adaptive dictionary is used to remove
artifacts, and an external Gaussian Mixture Model (GMM) dictionary, learned
from clean training images, is used to enhance details and texture. To make the
proposed method effective and robust, the split Bregman method is adopted to
reconstruct the whole image. Experimental results manifest the proposed
JGRSC-CS algorithm outperforms existing state-of-the-art methods in both peak
signal to noise ratio (PSNR) and visual quality.Comment: 27 pages, 7 figure
Highly Scalable Image Reconstruction using Deep Neural Networks with Bandpass Filtering
To increase the flexibility and scalability of deep neural networks for image
reconstruction, a framework is proposed based on bandpass filtering. For many
applications, sensing measurements are performed indirectly. For example, in
magnetic resonance imaging, data are sampled in the frequency domain. The
introduction of bandpass filtering enables leveraging known imaging physics
while ensuring that the final reconstruction is consistent with actual
measurements to maintain reconstruction accuracy. We demonstrate this flexible
architecture for reconstructing subsampled datasets of MRI scans. The resulting
high subsampling rates increase the speed of MRI acquisitions and enable the
visualization rapid hemodynamics.Comment: 9 pages, 10 figure
Group-based Sparse Representation for Image Compressive Sensing Reconstruction with Non-Convex Regularization
Patch-based sparse representation modeling has shown great potential in image
compressive sensing (CS) reconstruction. However, this model usually suffers
from some limits, such as dictionary learning with great computational
complexity, neglecting the relationship among similar patches. In this paper, a
group-based sparse representation method with non-convex regularization
(GSR-NCR) for image CS reconstruction is proposed. In GSR-NCR, the local
sparsity and nonlocal self-similarity of images is simultaneously considered in
a unified framework. Different from the previous methods based on
sparsity-promoting convex regularization, we extend the non-convex weighted Lp
(0 < p < 1) penalty function on group sparse coefficients of the data matrix,
rather than conventional L1-based regularization. To reduce the computational
complexity, instead of learning the dictionary with a high computational
complexity from natural images, we learn the principle component analysis (PCA)
based dictionary for each group. Moreover, to make the proposed scheme
tractable and robust, we have developed an efficient iterative
shrinkage/thresholding algorithm to solve the non-convex optimization problem.
Experimental results demonstrate that the proposed method outperforms many
state-of-the-art techniques for image CS reconstruction
Compressive Video Sensing via Dictionary Learning and Forward Prediction
In this paper, we propose a new framework for compressive video sensing (CVS)
that exploits the inherent spatial and temporal redundancies of a video
sequence, effectively. The proposed method splits the video sequence into the
key and non-key frames followed by dividing each frame into small
non-overlapping blocks of equal sizes. At the decoder side, the key frames are
reconstructed using adaptively learned sparsifying (ALS) basis via
minimization, in order to exploit the spatial redundancy. Also, the
effectiveness of three well-known dictionary learning algorithms is
investigated in our method. For recovery of the non-key frames, a prediction of
the current frame is initialized, by using the previous reconstructed frame, in
order to exploit the temporal redundancy. The prediction is employed in a
proper optimization problem to recover the current non-key frame. To compare
our experimental results with the results of some other methods, we employ peak
signal to noise ratio (PSNR) and structural similarity (SSIM) index as the
quality assessor. The numerical results show the adequacy of our proposed
method in CVS.Comment: 26 Pages, 5 Figures, 3 Tables, This paper was presented in part at
the 7th International Symposium on Telecommunications. arXiv admin note: text
overlap with arXiv:1404.7566 by other author
Compressive Sensing via Low-Rank Gaussian Mixture Models
We develop a new compressive sensing (CS) inversion algorithm by utilizing
the Gaussian mixture model (GMM). While the compressive sensing is performed
globally on the entire image as implemented in our lensless camera, a low-rank
GMM is imposed on the local image patches. This low-rank GMM is derived via
eigenvalue thresholding of the GMM trained on the projection of the measurement
data, thus learned {\em in situ}. The GMM and the projection of the measurement
data are updated iteratively during the reconstruction. Our GMM algorithm
degrades to the piecewise linear estimator (PLE) if each patch is represented
by a single Gaussian model. Inspired by this, a low-rank PLE algorithm is also
developed for CS inversion, constituting an additional contribution of this
paper. Extensive results on both simulation data and real data captured by the
lensless camera demonstrate the efficacy of the proposed algorithm.
Furthermore, we compare the CS reconstruction results using our algorithm with
the JPEG compression. Simulation results demonstrate that when limited
bandwidth is available (a small number of measurements), our algorithm can
achieve comparable results as JPEG.Comment: 12 pages, 8 figure
A survey of sparse representation: algorithms and applications
Sparse representation has attracted much attention from researchers in fields
of signal processing, image processing, computer vision and pattern
recognition. Sparse representation also has a good reputation in both
theoretical research and practical applications. Many different algorithms have
been proposed for sparse representation. The main purpose of this article is to
provide a comprehensive study and an updated review on sparse representation
and to supply a guidance for researchers. The taxonomy of sparse representation
methods can be studied from various viewpoints. For example, in terms of
different norm minimizations used in sparsity constraints, the methods can be
roughly categorized into five groups: sparse representation with -norm
minimization, sparse representation with -norm (0p1) minimization,
sparse representation with -norm minimization and sparse representation
with -norm minimization. In this paper, a comprehensive overview of
sparse representation is provided. The available sparse representation
algorithms can also be empirically categorized into four groups: greedy
strategy approximation, constrained optimization, proximity algorithm-based
optimization, and homotopy algorithm-based sparse representation. The
rationales of different algorithms in each category are analyzed and a wide
range of sparse representation applications are summarized, which could
sufficiently reveal the potential nature of the sparse representation theory.
Specifically, an experimentally comparative study of these sparse
representation algorithms was presented. The Matlab code used in this paper can
be available at: http://www.yongxu.org/lunwen.html.Comment: Published on IEEE Access, Vol. 3, pp. 490-530, 201
Measurement-Adaptive Sparse Image Sampling and Recovery
This paper presents an adaptive and intelligent sparse model for digital
image sampling and recovery. In the proposed sampler, we adaptively determine
the number of required samples for retrieving image based on
space-frequency-gradient information content of image patches. By leveraging
texture in space, sparsity locations in DCT domain, and directional
decomposition of gradients, the sampler structure consists of a combination of
uniform, random, and nonuniform sampling strategies. For reconstruction, we
model the recovery problem as a two-state cellular automaton to iteratively
restore image with scalable windows from generation to generation. We
demonstrate the recovery algorithm quickly converges after a few generations
for an image with arbitrary degree of texture. For a given number of
measurements, extensive experiments on standard image-sets, infra-red, and
mega-pixel range imaging devices show that the proposed measurement matrix
considerably increases the overall recovery performance, or equivalently
decreases the number of sampled pixels for a specific recovery quality compared
to random sampling matrix and Gaussian linear combinations employed by the
state-of-the-art compressive sensing methods. In practice, the proposed
measurement-adaptive sampling/recovery framework includes various applications
from intelligent compressive imaging-based acquisition devices to computer
vision and graphics, and image processing technology. Simulation codes are
available online for reproduction purposes
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