12,210 research outputs found
Confidence-aware Levenberg-Marquardt optimization for joint motion estimation and super-resolution
Motion estimation across low-resolution frames and the reconstruction of
high-resolution images are two coupled subproblems of multi-frame
super-resolution. This paper introduces a new joint optimization approach for
motion estimation and image reconstruction to address this interdependence. Our
method is formulated via non-linear least squares optimization and combines two
principles of robust super-resolution. First, to enhance the robustness of the
joint estimation, we propose a confidence-aware energy minimization framework
augmented with sparse regularization. Second, we develop a tailor-made
Levenberg-Marquardt iteration scheme to jointly estimate motion parameters and
the high-resolution image along with the corresponding model confidence
parameters. Our experiments on simulated and real images confirm that the
proposed approach outperforms decoupled motion estimation and image
reconstruction as well as related state-of-the-art joint estimation algorithms.Comment: accepted for ICIP 201
MRI Field-transfer Reconstruction with Limited Data: Regularization by Neural Style Transfer
Recent works have demonstrated success in MRI reconstruction using deep
learning-based models. However, most reported approaches require training on a
task-specific, large-scale dataset. Regularization by denoising (RED) is a
general pipeline which embeds a denoiser as a prior for image reconstruction.
The potential of RED has been demonstrated for multiple image-related tasks
such as denoising, deblurring and super-resolution. In this work, we propose a
regularization by neural style transfer (RNST) method to further leverage the
priors from the neural transfer and denoising engine. This enables RNST to
reconstruct a high-quality image from a noisy low-quality image with different
image styles and limited data. We validate RNST with clinical MRI scans from
1.5T and 3T and show that RNST can significantly boost image quality. Our
results highlight the capability of the RNST framework for MRI reconstruction
and the potential for reconstruction tasks with limited data.Comment: 30 pages, 8 figures, 2 tables, 1 algorithm char
Image reconstruction in optical interferometry: Benchmarking the regularization
With the advent of infrared long-baseline interferometers with more than two
telescopes, both the size and the completeness of interferometric data sets
have significantly increased, allowing images based on models with no a priori
assumptions to be reconstructed. Our main objective is to analyze the multiple
parameters of the image reconstruction process with particular attention to the
regularization term and the study of their behavior in different situations.
The secondary goal is to derive practical rules for the users. Using the
Multi-aperture image Reconstruction Algorithm (MiRA), we performed multiple
systematic tests, analyzing 11 regularization terms commonly used. The tests
are made on different astrophysical objects, different (u,v) plane coverages
and several signal-to-noise ratios to determine the minimal configuration
needed to reconstruct an image. We establish a methodology and we introduce the
mean-square errors (MSE) to discuss the results. From the ~24000 simulations
performed for the benchmarking of image reconstruction with MiRA, we are able
to classify the different regularizations in the context of the observations.
We find typical values of the regularization weight. A minimal (u,v) coverage
is required to reconstruct an acceptable image, whereas no limits are found for
the studied values of the signal-to-noise ratio. We also show that
super-resolution can be achieved with increasing performance with the (u,v)
coverage filling. Using image reconstruction with a sufficient (u,v) coverage
is shown to be reliable. The choice of the main parameters of the
reconstruction is tightly constrained. We recommend that efforts to develop
interferometric infrastructures should first concentrate on the number of
telescopes to combine, and secondly on improving the accuracy and sensitivity
of the arrays.Comment: 15 pages, 16 figures; accepted in A&
A total variation regularization based super-resolution reconstruction algorithm for digital video
Super-resolution (SR) reconstruction technique is capable of producing a high-resolution image from a sequence of low-resolution images. In this paper, we study an efficient SR algorithm for digital video. To effectively deal with the intractable problems in SR video reconstruction, such as inevitable motion estimation errors, noise, blurring, missing regions, and compression artifacts, the total variation (TV) regularization is employed in the reconstruction model. We use the fixed-point iteration method and preconditioning techniques to efficiently solve the associated nonlinear Euler-Lagrange equations of the corresponding variational problem in SR. The proposed algorithm has been tested in several cases of motion and degradation. It is also compared with the Laplacian regularization-based SR algorithm and other TV-based SR algorithms. Experimental results are presented to illustrate the effectiveness of the proposed algorithm.£.published_or_final_versio
A fast and accurate basis pursuit denoising algorithm with application to super-resolving tomographic SAR
regularization is used for finding sparse solutions to an
underdetermined linear system. As sparse signals are widely expected in remote
sensing, this type of regularization scheme and its extensions have been widely
employed in many remote sensing problems, such as image fusion, target
detection, image super-resolution, and others and have led to promising
results. However, solving such sparse reconstruction problems is
computationally expensive and has limitations in its practical use. In this
paper, we proposed a novel efficient algorithm for solving the complex-valued
regularized least squares problem. Taking the high-dimensional
tomographic synthetic aperture radar (TomoSAR) as a practical example, we
carried out extensive experiments, both with simulation data and real data, to
demonstrate that the proposed approach can retain the accuracy of second order
methods while dramatically speeding up the processing by one or two orders.
Although we have chosen TomoSAR as the example, the proposed method can be
generally applied to any spectral estimation problems.Comment: 11 pages, IEEE Transactions on Geoscience and Remote Sensin
Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction
Tuning the regularization hyperparameter in inverse problems has
been a longstanding problem. This is particularly true in the case of fetal
brain magnetic resonance imaging, where an isotropic high-resolution volume is
reconstructed from motion-corrupted low-resolution series of two-dimensional
thick slices. Indeed, the lack of ground truth images makes challenging the
adaptation of to a given setting of interest in a quantitative manner.
In this work, we propose a simulation-based approach to tune for a
given acquisition setting. We focus on the influence of the magnetic field
strength and availability of input low-resolution images on the ill-posedness
of the problem. Our results show that the optimal , chosen as the one
maximizing the similarity with the simulated reference image, significantly
improves the super-resolution reconstruction accuracy compared to the generally
adopted default regularization values, independently of the selected pipeline.
Qualitative validation on clinical data confirms the importance of tuning this
parameter to the targeted clinical image setting.Comment: 11 pages. This work has been submitted to MICCAI 202
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