nils-herrmann/clean_scirepeval huggingface dataset:
Ultrasound image de-noising through Karhunen-Loeve (K-L) transformwith overlapping segments

A new approach to filter out multiplicative noise from ultrasound images is presented in this paper. The noisy image is segmented into small segments, and the global covariance matrix is found. A projection matrix is formed by selecting the maximum eigenvectors of the global covariance matrix. This projection matrix is then used to filter noise by projecting the segment into the signal subspace. This approach is based on the fact that signal and noise are independent (orthogonal) and the signal subspace is spanned by a subset of the eigenvectors corresponding to the set of largest eigenvalues. When applied on simulated and real ultrasound images, our approach has outperformed popular nonlinear denoising techniques, such as Wavelets, Total Variation Filtering and Anisotropic Diffusion Filtering. It also showed less sensitivity to outliers resulted from the log transformation of the multiplicative noise.
MLS: Joint manifold-learning and sparsity-aware framework for highly accelerated dynamic magnetic resonance imaging

Manifold-based models have been recently exploited for accelerating dynamic magnetic resonance imaging (dMRI). While manifold-based models have shown to be more efficient than conventional low-rank approaches, joint low-rank and sparsity-aware modeling still appears to be very efficient due to the inherent sparsity within dMR images. In this paper, we propose a joint manifold-learning and sparsity-aware framework for dMRI. The proposed method establishes a link between the recently developed manifold models and conventional sparsity-aware models. Dynamic MR images are modeled as points located on or close to a smooth manifold, and a novel data-driven manifold-learning approach, which preserves affine relation among images, is used to learn the low-dimensional embedding of the dynamic images. The temporal basis learnt from such an approach efficiently captures the inherent periodicity of dynamic images and hence sparsity along temporal direction is enforced during reconstruction. The proposed framework is validated by extensive numerical tests on phantom and in-vivo data sets.
Image reconstruction from phased-array MRI data based on multichannel blind deconvolution

In this paper we consider image reconstruction from multichannel phased array MRI data without prior knowledge of the coil sensitivity functions. A new framework based on multichannel blind deconvolution (MBD) is developed for joint estimation of the image function and the sensitivity functions in k-space. By exploiting the smoothness of the estimated functions in the spatial domain, we develop a regularization approach in conjunction with MBD to obtain good reconstruction of the image function. Experimental results using simulated and real data demonstrate that the proposed reconstruction algorithm can better removes the sensitivity weighting in the reconstructed images compared to the sum-of-squares (SoS) approach.
Accelerating magnetic resonance imaging via deep learning

This paper proposes a deep learning approach for accelerating magnetic resonance imaging (MRI) using a large number of existing high quality MR images as the training datasets. An off-line convolutional neural network is designed and trained to identify the mapping relationship between the MR images obtained from zero-filled and fully-sampled k-space data. The network is not only capable of restoring fine structures and details but is also compatible with online constrained reconstruction methods. Experimental results on real MR data have shown encouraging performance of the proposed method for efficient and accurate imaging.
A hybrid total-variation minimization approach to compressed sensing

Compressed sensing (CS) has been successfully applied to accelerate conventional magnetic resonance imaging (MRI) with Fourier encoding. Total variation (TV) is usually used as the regularization function for image reconstruction. However, it is know that such ℓ<sub>1</sub>-based minimization algorithm needs more measurements than the ℓ<sub>0</sub>-based ones. On the other hand, ℓ<sub>0</sub>-based minimization is computational intractable and unstable. In this paper, we propose a hybrid total variation (HTV) which effectively integrates both ℓ<sub>1</sub>-norm and ℓ<sub>0</sub>-norm of the image gradient by introducing a threshold. The HTV minimization algorithm has the benefits of both the robustness of ℓ<sub>1</sub> and fewer measurements of ℓ<sub>0</sub>. Simulations and in vivo experiments demonstrate the proposed method outperforms the conventional TV minimization algorithm.
Adaptive Dictionary Learning in Sparse Gradient Domain for Image Recovery

Image recovery from undersampled data has always been challenging due to its implicit ill-posed nature but becomes fascinating with the emerging compressed sensing (CS) theory. This paper proposes a novel gradient based dictionary learning method for image recovery, which effectively integrates the popular total variation (TV) and dictionary learning technique into the same framework. Specifically, we first train dictionaries from the horizontal and vertical gradients of the image and then reconstruct the desired image using the sparse representations of both derivatives. The proposed method enables local features in the gradient images to be captured effectively, and can be viewed as an adaptive extension of the TV regularization. The results of various experiments on MR images consistently demonstrate that the proposed algorithm efficiently recovers images and presents advantages over the current leading CS reconstruction approaches.
Learning Joint-Sparse Codes for Calibration-Free Parallel MR Imaging

The integration of compressed sensing and parallel imaging (CS-PI) has shown an increased popularity in recent years to accelerate magnetic resonance (MR) imaging. Among them, calibration-free techniques have presented encouraging performances due to its capability in robustly handling the sensitivity information. Unfortunately, existing calibration-free methods have only explored joint-sparsity with direct analysis transform projections. To further exploit joint-sparsity and improve reconstruction accuracy, this paper proposes to Learn joINt-sparse coDes for caliBration-free parallEl mR imaGing (LINDBERG) by modeling the parallel MR imaging problem as an <inline-formula> <tex-math notation="LaTeX">$\ell _{\textsf {2}}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$\ell _{F}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$\ell _{\textsf {2},\textsf {1}}$ </tex-math></inline-formula> minimization objective with an <inline-formula> <tex-math notation="LaTeX">$\ell _{\textsf {2}}$ </tex-math></inline-formula> norm constraining data fidelity, Frobenius norm enforcing sparse representation error and the <inline-formula> <tex-math notation="LaTeX">$\ell _{\textsf {2},\textsf {1}}$ </tex-math></inline-formula> mixed norm triggering joint sparsity across multichannels. A corresponding algorithm has been developed to alternatively update the sparse representation, sensitivity encoded images and K-space data. Then, the final image is produced as the square root of sum of squares of all channel images. Experimental results on both physical phantom and in vivo data sets show that the proposed method is comparable and even superior to state-of-the-art CS-PI reconstruction approaches. Specifically, LINDBERG has presented strong capability in suppressing noise and artifacts while reconstructing MR images from highly undersampled multichannel measurements.
Integrating Parallel Imaging with Generalized Series for Accelerated Dynamic Imaging

One key problem in MR dynamic imaging (e.g. dynamic contrast-enhanced (DCE) imaging of breast cancer) is low spatiotemporal resolution. To tackle this problem, this paper presents a novel method to integrate parallel imaging using multiple receiver coils with generalized series (GS) imaging. The proposed method takes advantage of both the conventional parallel data acquisition scheme and the GS model-based imaging method to achieve higher spatiotemporal resolution in dynamic imaging. Simulations on human breast cancer imaging and mammary tumor imaging of rat and the experiment on DCE imaging of human chest tumors yielded excellent results by the proposed method
Noise behavior of MR brain reconstructions using compressed sensing

Compressed sensing (CS) has demonstrated great potential to reconstruct high quality MR images from undersampled k-space data. However, successful application of CS in clinic is still limited by many factors. One of the key factors is that the noise behavior in CS reconstructions remains largely unexplored. The main objective of this work is to analyze the noise behavior of MR reconstructions using CS method with different reduction factors. Our work focuses on brain CS-MRI reconstructions using non-linear conjugate gradient (NLCG) solvers. After reconstruction, the noise behavior is characterized using the MP-Law method. The results show that the spatial noise distributed non-uniformly, and the noise variance from CS reconstruction increases with reduction factors. A kind of fitting model is given, which can be used to predict the noise behavior parameter for different reduction factors, and the noise amplification factor maps are shown to prove the denoising capability of CS reconstruction. The results provide a qualitative and quantitative understanding of the noise behavior in CS-MRI with different reduction factors.
Parallel MRI Acceleration Using M-FOCUSS

Parallel magnetic resonance imaging (pMRI) cannot achieve its maximum reduction factor due to practical limitations. The combination of pMRI and distributed compressed sensing (DCS) for further acceleration is of great interest. In this paper, we propose a method to combine sensitivity encoding (SENSE), one of the standard methods for pMRI, and M-FOCUSS, an algorithm solving DCS reconstruction problem. The proposed method first employs M-FOCUSS algorithm to simultaneously reconstruct a set of aliased reduced field-of-view (FOV) images for each channel, and then applies cartesian SENSE to reconstruct the final image. The experimental results demonstrate that the proposed method outperforms the existing methods with the same reduction factor.
Accelerating MR parameter mapping using nonlinear manifold learning and supervised pre-imaging

In this paper, we propose a new reconstruction framework that utilizes nonlinear models to sparsely represent the MR parameter-weighted image in a high dimensional feature space. Different from the prior work with nonlinear models where the image series is reconstructed simultaneously, each image at a specific time point is assumed to lie in a low-dimensional manifold and is reconstructed individually. The low-dimensional manifold is learned from the training images generated by the parametric model. To reconstruct each image, among infinite number of solutions that satisfy the data consistent constraint, the one that is closest to the manifold is selected as the desired solution. The underlying optimization problem is solved using kernel trick and split Bregman iteration algorithm. The proposed method was evaluated on a set of in-vivo brain T2 mapping data set and shown to be superior to the conventional compressed sensing methods.
Dynamic magnetic resonance imaging using compressed sensing with self-learned nonlinear dictionary (NL-D)

Compressed Sensing (CS) is a new paradigm in signal processing and reconstruction from sub-nyquist sampled data. CS has shown promising results in accelerating dynamic Magnetic Resonance Imaging (dMRI). CS based approaches hugely rely on sparsifying transforms to reconstruct the dynamic MR images from its undersampled k-space data. Recent developments in dictionary learning and nonlinear kernel based methods have shown to be capable of representing dynamic images more sparsely than conventional linear transforms. In this paper, we propose a novel method (NL-D) to represent the dMRI more sparsely using self-learned nonlinear dictionaries based on kernel methods. Based on the proposed model, a new iterative approach for image reconstruction relying on pre-image reconstruction is developed within CS framework. Simulation results have shown that the proposed method outperforms the conventional CS approaches based on linear sparsifying transforms.
Recovery of parametric manifold from reduced measurements: Application to magnetic resonance parameter mapping

Magnetic resonance (MR) quantitative imaging such as parameter mapping has great potential in clinical applications. It requires acquisition of a sequence of images at different time points to extract the quantitative parameters. A practical challenge is the tradeoff between spatial resolution and acquisition speed. A number of methods have been proposed to address the challenge. In this paper, a novel manifold recovery approach is proposed to obtain the quantitative map from highly reduced measurements. The performance of the proposed method is demonstrated using simulated and real datasets on parameter mapping.
A Kernel-Based Low-Rank (KLR) Model for Low-Dimensional Manifold Recovery in Highly Accelerated Dynamic MRI

While many low rank and sparsity-based approaches have been developed for accelerated dynamic magnetic resonance imaging (dMRI), they all use low rankness or sparsity in input space, overlooking the intrinsic nonlinear correlation in most dMRI data. In this paper, we propose a kernel-based framework to allow nonlinear manifold models in reconstruction from sub-Nyquist data. Within this framework, many existing algorithms can be extended to kernel framework with nonlinear models. In particular, we have developed a novel algorithm with a kernel-based low-rank model generalizing the conventional low rank formulation. The algorithm consists of manifold learning using kernel, low rank enforcement in feature space, and preimaging with data consistency. Extensive simulation and experiment results show that the proposed method surpasses the conventional low-rank-modeled approaches for dMRI.
Compressed-sensing dynamic MR imaging with partially known support

Compressed Sensing (CS) has recently been applied to dynamic MRI to improve the acquisition speed. Existing methods exploit the information that the dynamic images are sparse in the spatial and temporal-frequency (y-f) domain. In this paper, we propose to use the additional prior information in CS reconstruction that the support of y-f space is partially known from the motion pattern of dynamic MR images. The reconstruction is then formulated as a truncated ℓ1 minimization problem. Experimental results show that the dynamic image reconstruction quality of the proposed method is superior to that of existing methods when the same number of measurements is used.
Highly accelerated dynamic contrast-enhanced MRI with temporal constrained reconstruction

For DCE MRI applications, the images of adjacent time frames are often similar, especially when motion is minimal, in which case temporal TV is a reasonable regularization term. Temporal constraint reconstruction (TCR) has been developed to reconstruct dynamic images from undersampled k-t space data based on such prior information. However, the convergence speed of the algorithm highly depends on the initialization method. In this study, we study initialization using a composite high resolution image based on a jigsaw sampling pattern during pre-contrast frames. The proposed initialization method converges much faster than a conventional initialization method using low resolution images, especially at high reduction factors. In vivo breast imaging experiments were carried out to evaluate the performance of the proposed method. Experiments show the new initialization method allows TCR to achieve a high reduction factor up to 40 without compromising much of the spatial or temporal resolution. The reconstruction errors are much lower than those using the low resolution initialization when the same number of measurements is used.
Compressed Sensing Dynamic Cardiac Cine MRI Using Learned Spatiotemporal Dictionary

In dynamic cardiac cine magnetic resonance imaging, the spatiotemporal resolution is limited by the low imaging speed. Compressed sensing (CS) theory has been applied to improve the imaging speed and thus the spatiotemporal resolution. In this paper, we propose a novel technique that employs a patch-based 3-D spatiotemporal dictionary for sparse representations of dynamic image sequence in the CS framework. Specifically, the dynamic image sequence is divided into overlapping patches along both the spatial and temporal directions. The dictionary is used to provide flexible sparse expressions for these patches. The underlying optimization problem is solved by variable splitting and the alternating direction method with multiplier. Experimental results based on in vivo cardiac data demonstrate that the proposed method is able to accelerate cardiac cine imaging by a factor up to 8 and outperforms the existing state-of-the-art CS methods at high accelerations. The method is expected to be useful in dynamic imaging with a higher spatiotemporal resolution.
Controlling the manipulator of an underwater ROV using a coarse calibrated pan/tilt camera

We present a vision-based method to control the displacement of robot arm mounted on an underwater remotely operated vehicle (ROV). A closed-loop system based on an eye-to-hand visual servoing approach has been designed to achieve this task. We show that, using such an approach, the measuring of the manipulator motion with proprioceptive sensors is not required to precisely control the end-effector motion. To maintain the end effector in the field of view, the camera orientation is also controlled. The results presented show the validity of the approach.
From data-flow task to multitasking: applying the synchronous approach to active vision in robotics

We apply the synchronous approach to real time active visual three dimensional (3D) reconstruction using a camera mounted on a robot end effector. It illustrates the adequateness of SIGNAL, a synchronous data flow programming language and environment, for the specification of a system dealing with various domains in central theory and computer vision. More precisely, our application consists in the 3D structure estimation of a set of geometrical primitives using an active vision paradigm. At the level of camera motion control, the visual servoing approach is specified and implemented in SIGNAL as a function from sensor inputs to control outputs. Furthermore, the 3D structure estimation method is based on the "structure from controlled motion" approach (constraining camera motion for optimal estimation). Its specification is made in parallel to visual servoing, and involves the delay mechanism of SIGNAL for the specification of filters. This reconstruction involves to be focused on each object; we thus present a perception strategy for sequencing several estimations, using task preemption and time intervals in SIGNAL. It allows us to consider in an unified framework the various aspects of the application: from dataflow task specification to multitasking and hierarchical task preemption. The integration of these techniques is validated experimentally by their implementation on a robotic cell. Merits and drawbacks of the proposed framework with respect to more typical asynchronous approaches are also discussed.
Controlled camera motions for scene reconstruction and exploration

This paper deals with the 3D structure estimation and exploration of a scene using active vision. Our method is based on the structure from controlled motion approach which consists in constraining the camera motion in order to obtain a precise and robust estimation of the 3D structure of a geometrical primitive. Since this approach involves to gaze on the considered primitive, we present a method for connecting up many estimations in order to recover the complete spatial structure of scenes composed of cylinders and segments. We have developed perceptual strategies able to perform a succession of robust estimations without any assumption on the number and on the localization of the different objects. Furthermore, the proposed strategy ensures the completeness of the reconstruction. An exploration process centered on current visual features and on the structure of the previously studied primitives is presented. This leads to a gaze planning strategy that mainly uses a representation of known and unknown areas as a basis for selecting viewpoints. Finally, experiments carried out on a robotic cell have proved the validity of our approach.
Image-based UAV localization using interval methods

This paper proposes an image-based localization method that enables to estimate a bounded domain of the pose of an unmanned aerial vehicle (UAV) from uncertain measurements of known landmarks in the image. The approach computes a domain that should contain the actual robot pose, assuming bounded image measurement errors and landmark position uncertainty. It relies on interval analysis and constraint propagation techniques to rigorously back-propagate the errors through the non-linear observation model. Attitude information from onboard sensors is merged with image observations to reduce the pose uncertainty domain, along with prediction based on velocity measurements. As tracking landmarks in the image is prone to errors, the proposed method also enable fault detection from measurement inconsistencies. This method is tested using a quadcopter UAV with an onboard camera.
Real time planar structure tracking for visual servoing: a contour and texture approach

Robustness and accuracy are major issues in real-time object tracking in image sequences. This paper describes a reliable tracking for markerless objects based on the fusion of visual cues and on the estimation of a 2D transformation. The parameters of this transformation are estimated using a non-linear minimization of a unique criterion that integrates information both on the texture and the edges of the tracked object. The proposed tracker is then more robust and succeeds in conditions where methods based on a single cue fail. The efficiency and the robustness of the proposed method are tested on image sequences as well as during an image-based visual servoing experiment.
Video mosaicing using a mutual information-based motion estimation process

This paper proposes a method to achieve parametric motion estimation based on mutual information and its use in video mosaicing applications. Sum of Squared Differences (SSD) is widely considered in motion estimation. Here, we consider another metric, Mutual Information (MI), which is far less sensitive to changes in the lighting condition, to occlusions, and to a wide class of non-linear image transformation. Results under various complex conditions are presented.
A Direct Visual Servoing Scheme for Automatic Nanopositioning

This paper demonstrates an accurate nanopositioning scheme based on a direct visual servoing process. This technique uses only the pure image signal (photometric information) to design the visual servoing control law. With respect to traditional visual servoing approaches that use geometric visual features (points, lines ...), the visual features used in the control law is the pixel intensity. The proposed approach has been tested in term of accuracy and robustness in several experimental conditions. The obtained results have demonstrated a good behavior of the control law and very good positioning accuracy. The obtained accuracies are 89 nm, 14 nm, and 0.001° in the x-, y -, and θ-axes of a positioning platform, respectively.