nils-herrmann/clean_scirepeval huggingface dataset:
Magnetic Field Design of Coil-Dominated Magnets Wound With Coated Conductors

Coil-dominated magnets wound with coated conductors were designed for an FFAG accelerator for carbon therapy, which was designed by applying linear approximation. When designing the coil-end of the magnets, we applied differential geometry. To apply the differential geometry to three-dimensional windings with coated conductors, we introduced the concept of generalized flat-wise bending. Thereby, the combination of flat-wise bending and torsion was considered as bending of developable surface. Generated magnetic field was calculated, and the positions of conductors were optimized to minimize the field error. Three dimensional shapes of magnets were successfully designed with constraints on generalized flat-wise bending and edge-wise bending of coated conductors.
Reconstruction of compressively sampled light fields using a weighted 4D-DCT basis

The coded aperture/mask technique enables us to capture light field data in a compressive way through a single camera. A pixel value recorded by such a camera is a summation of the light rays that pass though different positions on the coded aperture/mask. The target light field can be reconstructed from the recorded pixel values by using prior information of the light field signal. As prior information, a dictionary (light field atoms), which was learned from training datasets, was used in the current state of the art. Meanwhile, it was reported that general bases such as DCT were not suitable to efficiently represent prior information. In this work, however, we demonstrate that a 4D-DCT basis works surprisingly better if it is combined with a weighting scheme in which the amplitude difference in DCT coefficients is considered. Simulation results using 18 light field datasets are reported to show the superior performance of the weighted 4D-DCT basis to the learned dictionary.
Joint directional-positional multiplexing for light field acquisition by Kronecker compressed sensing

In this paper, we propose a joint and unified framework to compressively capture a light field in the consideration of both directional and positional multiplexing based on Kronecker compressed sensing (KCS). First of all, both of the 2D angular and 2D spatial correlations of the light field can be fully utilized in the compressive acquisition, and the multiplexing is more flexible and balanced during the acquisition. Secondly, other types of light field acquisition can be unified into our proposed framework. In the experiment, it is shown that more balanced allocation between directional and positional multiplexing achieves better reconstruction quality of light field given the same number of total acquisitions. Furthermore, the experimental result also illustrates that the proposed method can capture a light field with full resolution and achieve better reconstruction quality than other previous methods.
Good group sparsity prior for light field interpolation

A light field, which is equivalent to a dense set of multi-view images, has various applications such as depth estimation and 3D display. One of the essential problems is light field interpolation, which is obtaining sufficiently dense views from sparser views. The accuracy of interpolation will be enhance by exploiting an inherent property of a light field. Specifically, an epipolar plane image (EPI), which is a 2D subset of the 4D light field, consists of many lines. This structure induces a sparse representation in the frequency domain, where most of the energy resides on a line passing through the origin. On the basis of this observation, we propose a group sparsity prior suitable for light fields to fully exploit their line structure for interpolation. Our experimental results show that the proposed method can achieve better quality than a state-of-the-art shearlet-based method.
Seam carving for stereo images

We propose a content-preserving stereo image editing technique by using stereo extension of seam carving. Seam carving is the process of deleting or duplicating connected paths, or seams, that consist of less important pixels in order to resize the image while preserving the image content as much as possible. The problem targeted in this paper is how to apply the seam carving method to a pair of stereo images, where the consistency between the left and right images should also be preserved through the seam carving process. For this consistency, we introduce new energy terms. Based on these energy terms, seam pairs between the input images are classified into two types, corresponding seams in order to maintain consistency or occluded seams to change the consistency intentionally. The novelty of this paper is that stereo matching results are fused into the framework in the seam carving. We demonstrate that by selecting seams in an appropriate way we can virtually manipulate the depths of objects in the scene.
An efficient variational multiphase motion for the Mumford-Shah segmentation model

We propose a new multiphase level set model for image segmentation, using Mumford-Shah techniques in the piecewise-constant case. The proposed model is also a generalization of our active contour model without edges based on a two-phase segmentation. In order to handle multiple individual segments and complex topologies (such as multiple junctions), we propose a new and efficient multiphase representation by level sets: the necessary number of level set functions is considerably reduced (we need only n level set functions to represent 2/sup n/ phases or segments), and in addition we have overcome the problems of vacuum and overlap, naturally arising in multiphase problems. Finally, we show how the model can be used in image segmentation, for synthetic and real (possible noisy) pictures.
Tetrahedral mesh generation for medical images with multiple regions using active surfaces

In this paper, we present a method for automatically generating tetrahedral meshes from 3D images with multiple region labels. The first step consists of constructing an adaptively sized tetrahedral mesh that conforms exactly to the voxelized regions in the image. Active surfaces (active contours in 2D) are then employed to smooth the region boundaries and remove the voxelization. Specifically, an energy with three terms is minimized: a smoothing term to remove the voxelization, a fidelity term to keep the mesh from moving too far away from the image data, and an elasticity term to keep the tetrahedra from becoming flattened or inverted as the mesh deforms. The algorithm for tetrahedral mesh generation is applied to an MRI image that has been automatically segmented using an existing method. The resulting mesh has a number of desirable properties such as tetrahedra with all dihedral angles away from 0 and 180 degrees, smoothness, and a high level of detail for the number of tetrahedra used.
A convex minimization model in image restoration via one-dimensional Sobolev norm profiles

We propose a new variational model for image restoration using BV and Sobolev spaces. It is well known that homogeneous Sobolev spaces of negative differentiability can capture oscillatory information very well, however, just one Sobolev space hardly recognizes any difference between texture and noise. By a means of learning a series of Sobolev norms of pure texture and pure noise that will provide us with one dimensional profiles describing different behaviors of texture and noise, we will be able to make a distinction between texture and noise, and use these measurements in restoring a better image. We want to point out that our model is specifically designed to deal with noisy blurred images. Parameter insen-sitivity is one of the advantages of using a series of Sobolev spaces.
Platform characterization for Domain-Specific Computing

We believe that by adapting architectures to fit the requirements of a given application domain, we can significantly improve the efficiency of computation. To validate the idea for our application domain, we evaluate a wide spectrum of commodity computing platforms to quantify the potential benefits of heterogeneity and customization for the domain-specific applications. In particular, we choose medical imaging as the application domain for investigation, and study the application performance and energy efficiency across a diverse set of commodity hardware platforms, such as general-purpose multi-core CPUs, massive parallel many-core GPUs, low-power mobile CPUs and fine-grain customizable FPGAs. This study leads to a number of interesting observations that can be used to guide further development of domain-specific architectures.
Shape Representation based on Integral Kernels: Application to Image Matching and Segmentation

This paper presents a shape representation and a variational framework for the construction of diffeomorphisms that establish "meaningful"correspondences between images, in that they preserve the local geometry of singularities such as region boundaries. At the same time, the shape representation allows enforcing shape information locally in determining such region boundaries. Our representation is based on a kernel descriptor that characterizes local shape. This shape descriptor is robust to noise and forms a scale-space in which an appropriate scale can be chosen depending on the size of features of interest in the scene. In order to preserve local shape during the matching procedure, we introduce a novel constraint to traditional energybased approaches to estimate diffeomorphic deformations, and enforce it in a variational framework.
A Hybrid Architecture for Compressive Sensing 3-D CT Reconstruction

The radiation dose associated with computerized tomography (CT) is significant. Compressive sensing (CS) methods provide mathematical approaches to reduce the radiation exposure without sacrificing reconstructed image quality. However, the computational requirements of these algorithms is much higher than conventional image reconstruction approaches such as filtered back projection (FBP). This paper describes a new compressive sensing 3-D image reconstruction algorithm based on expectation maximization and total variation, termed EM+TV, and also introduces a promising hybrid architecture implementation for this algorithm involving the combination of a CPU, GPU, and FPGA. An FPGA is used to speed up the major computation kernel (EM), and a GPU is used to accelerate the TV operations. The performance results indicate that this approach provides lower energy consumption and better reconstruction quality, and illustrates an example of the advantages that can be realized through domain-specific computing.
Segmentation using the edge strength function as a shape prior within a local deformation model

This paper presents a new image segmentation framework which employs a shape prior in the form of an edge strength function to introduce a higher-level influence on the segmentation process. We formulate segmentation as the minimization of three coupled functionals, respectively, defining three processes: prior-guided segmentation, shape feature extraction and local deformation estimation. Particularly, the shape feature extraction process is in charge of estimating an edge strength function from the evolving object region. The local deformation estimation process uses this function to determine a meaningful correspondence between a given prior and the evolving object region, and the deformation map estimated in return supervises the segmentation by enforcing the evolving object boundary towards the prior shape.
Efficient Deconvolution and Super-Resolution Methods in Microwave Imagery

In this paper, we develop efficient deconvolution and super-resolution methodologies, and apply these techniques to reduce image blurring and distortion inherent in an aperture synthesis system. Such a system produces ringing at sharp edges and other transitions in the observed field. The conventional approach to suppressing sidelobes is to apply linear apodization, which has the undesirable side effect of degrading spatial resolution. We have developed an efficient total variation minimization technique based on Split Bregman deconvolution that reduces image ringing while sharpening the image and preserving information content. The model was generalized to include upsampling of deconvolved images to a higher resolution grid. Furthermore, a proposed multiframe super-resolution method is presented that is robust to image noise and noise in the point spread function, and leads to additional improvements in spatial resolution. Our super-resolution methodologies are based on current research in sparse optimization and compressed sensing, which lead to unprecedented efficiencies for solving image reconstruction problems.
A new framework of quantifying differences between images by matching gradient fields and its application to image blending

This paper presents a new method for quantifying the differences between images. The proposed model is based on matching the gradient fields of two images. We first define new image spaces in which images are considered equivalent under a similarity group actions and the difference between two image classes is then defined by employing the Cauchy-Schwarz inequality to the gradient fields. The advantage of our approach is that images are identified by their relative contrasts and thus is scale free. Using this approach, we are able to achieve image blending in a novel way. By modifying the group actions, we extend our basic model to more general equivalence classes. The variational problems and the corresponding Euler-Lagrange equations associated to these models are proposed and the gradient descent time dependent partial differential equations are derived. Fast and efficient solvers employing the Additive Operator Splitting scheme are also presented. We tested our models on simulation images as well as real brain MRI and PET images from normal control subjects.
Color Texture Modeling and Color Image Decomposition in a Variational-PDE Approach

This paper is devoted to a new variational model for color texture modeling and color image decomposition into cartoon and texture. A given image in the RGB space is decomposed into a cartoon part and a texture part. The cartoon part is modeled by the space of vector-valued functions of bounded variation, while the texture or noise part is modeled by a space of oscillatory functions, dual in some sense of the BV space. Examples for color image decomposition, color image denoising, and color texture discrimination and segmentation will be presented
Multiphase Segmentation of Deformation using Logarithmic Priors

In , the authors proposed the large deformation log-unbiased diffeomorphic nonlinear image registration model which has been successfully used to obtain theoretically and intuitively correct deformation maps. In this paper, we extend this idea to simultaneously registering and tracking deforming objects in a sequence of two or more images. We generalize a level set based Chan-Vese multiphase segmentation model to consider Jacobian fields while segmenting regions of growth and shrinkage in deformations. Deforming objects are thus classified based on magnitude of homogeneous deformation. Numerical experiments demonstrating our results include a pair of two-dimensional synthetic images and pairs of two-dimensional and three-dimensional serial MRI images.
Active contours without edges

We propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by the gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes a "mean-curvature flow"-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We give a numerical algorithm using finite differences. Finally, we present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected.
Image decomposition, image restoration, and texture modeling using total variation minimization and the H/sup -1/ norm

We propose a new model for image restoration and decomposition, based on the total variation minimization of Rudin-Osher-Fatemi (1992), and on some new techniques by Y. Meyer (2002) for oscillatory functions. An initial image f is decomposed into a cartoon part u and a texture or noise part v. The u component is modeled by a function of bounded variation, while the v component by an oscillatory function, with bounded H/sup -1/ norm. After some transformation, the resulting PDE is of fourth order. The proposed model continues the ideas and techniques previously introduced by the authors in L Vese et al., (2002). Image decomposition and denoising numerical results will be shown by the proposed new fourth order nonlinear partial differential equation.
New framework for object warping: semi-Lagrangian level set approach

In this paper, we present a new framework for object matching between two images. This method could handle multiple pairs of overlapping and non-overlapping shapes, open curves, and landmarks. When implemented in 3-D, the same framework could be used to warp 3-D objects with minimal modification. Our approach is to use the level set formulation to represent the objects to be matched. Using this representation, the problem becomes an energy minimization problem. Cost functions for warping overlapping, non-overlapping, open curves, and landmarks are proposed. Euler-Lagrange equations are applied and gradient descent is used to solve the corresponding partial differential equations. Moreover, a general framework for linking the level set approach and the infinite dimensional group actions is discussed.
Multiframe image restoration in the presence of noisy blur kernel

We wish to recover an original image u from several blurry-noisy versions fk, called frames. We assume a more severe degradation model, in which the image u has been blurred by a noisy (stochastic) point spread function. We consider the problem of restoring the degraded image in a variational framework. Since the recovery of u from one single frame f is a highly ill-posed problem, we formulate two minimization problems based on the multiframe approach proposed for image super-resolution by Marquina-Osher [13]. Several experimental results for image restoration are shown, illustrating that the proposed models give visually satisfactory results.
Fast Cartoon + Texture Image Filters

Can images be decomposed into the sum of a geometric part and a textural part? In a theoretical breakthrough, [Y. Meyer, Oscillating Patterns in Image Processing and Nonlinear Evolution Equations. Providence, RI: American Mathematical Society, 2001] proposed variational models that force the geometric part into the space of functions with bounded variation, and the textural part into a space of oscillatory distributions. Meyer's models are simple minimization problems extending the famous total variation model. However, their numerical solution has proved challenging. It is the object of a literature rich in variants and numerical attempts. This paper starts with the linear model, which reduces to a low-pass/high-pass filter pair. A simple conversion of the linear filter pair into a nonlinear filter pair involving the total variation is introduced. This new-proposed nonlinear filter pair retains both the essential features of Meyer's models and the simplicity and rapidity of the linear model. It depends upon only one transparent parameter: the texture scale, measured in pixel mesh. Comparative experiments show a better and faster separation of cartoon from texture. One application is illustrated: edge detection.
Linear and non-linear geometric object matching with implicit representation

This paper deals with the matching of geometric objects including points, curves, surfaces, and subvolumes using implicit object representations in both linear and non-linear settings. This framework can be applied to feature-based non-linear image warping in biomedical imaging with the deformation constrained to be one-to-one, onto, and diffeomorphic. Moreover, a theoretical connection is established between the well known Hausdorff metric and the framework proposed in this paper. A general strategy for matching geometric objects in both 2D and 3D is discussed. The corresponding Euler-Lagrange equations are presented and gradient descent method is employed to solve the time dependent partial differential equations.
Boundary detection in echocardiography using a Split Bregman edge detector and a topology preserving level set approach

In the current paper a novel approach for echocardiographic segmentation is proposed based on a combination of the Geometric Active Contour Split Bregman (GSB) method with a topology preserving level set method. The proposed method was tested against manual delineations on 20 frames across 2 datasets and achieved an average Hausdorff distance of 4.01 ± 1.06 mm and Mean Absolute distance of 1.62 ± 0.3 mm, which represented an enhanced performance when compared with intensity gradient and region based methods.
Self-Repelling Snakes for Topology-Preserving Segmentation Models

The implicit framework of the level-set method has several advantages when tracking propagating fronts. Indeed, the evolving contour is embedded in a higher dimensional level-set function and its evolution can be phrased in terms of a Eulerian formulation. The ability of this intrinsic method to handle topological changes (merging and breaking) makes it useful in a wide range of applications (fluid mechanics, computer vision) and particularly in image segmentation, the main subject of this paper. Nevertheless, in some applications, this topological flexibility turns out to be undesirable: for instance, when the shape to be detected has a known topology, or when the resulting shape must be homeomorphic to the initial one. The necessity of designing topology-preserving processes arises in medical imaging, for example, in the human cortex reconstruction. It is known that the human cortex has a spherical topology so throughout the reconstruction process this topological feature must be preserved. Therefore, we propose in this paper a segmentation model based on an implicit level-set formulation and on the geodesic active contours, in which a topological constraint is enforced.
A level set algorithm for minimizing the Mumford-Shah functional in image processing

We show how the piecewise-smooth Mumford-Shah segmentation problem can be solved using the level set method of Osher and Sethian (1988). The obtained algorithm can be simultaneously used to denoise, segment, detect-extract edges, and perform active contours. The proposed model is also a generalisation of a previous active contour model without edges, proposed by the authors in Chan et al., (2001), and of its extension to the case with more than two segments for piecewise-constant segmentation Chan et al., (2000). Based on the four color theorem, we can assume that in general, at most two level set functions are sufficient to detect and represent distinct objects of distinct intensities, with triple junctions, or T-junctions.