- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Piecewise uniform regularization for the inverse problem of microtomography with a-posteriori error estimate
摘要： An inverse microtomography problem is under consideration in a class of functions with bounded V H variation. An algorithm for solving this problem is proposed based on Tikhonov’s regularization with a special regularizer. The algorithm ensures piecewise uniform convergence of approximate solutions to exact solution of the inverse problem. In addition, the question of a-posteriori error estimate of approximate solutions obtained is considered. A new numerical algorithm for finding this estimate is proposed. Numerical experiments on solving a model inverse problem on the class of functions with bounded V H variation are presented along with the results of a-posteriori error estimate for approximate solutions obtained.
关键词： microtomography,regularization,a-posteriori error estimates
更新于2025-09-23 15:23:52
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An Image Segmentation Method Based on Improved Regularized Level Set Model
摘要： When the level set algorithm is used to segment an image, the level set function must be initialized periodically to ensure that it remains a signed distance function (SDF). To avoid this defect, an improved regularized level set method-based image segmentation approach is presented. First, a new potential function is defined and introduced to reconstruct a new distance regularization term to solve this issue of periodically initializing the level set function. Second, by combining the distance regularization term with the internal and external energy terms, a new energy functional is developed. Then, the process of the new energy functional evolution is derived by using the calculus of variations and the steepest descent approach, and a partial differential equation is designed. Finally, an improved regularized level set-based image segmentation (IRLS-IS) method is proposed. Numerical experimental results demonstrate that the IRLS-IS method is not only effective and robust to segment noise and intensity-inhomogeneous images but can also analyze complex medical images well.
关键词： image segmentation,energy functional,level set,distance regularization term
更新于2025-09-23 15:23:52
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Full wave analysis of plane wave diffraction by a finite sinusoidal grating: E-polarization case
摘要： Plane wave diffraction studies of a finite sinusoidal grating have previously assumed that the grating length is large in wavelengths and the depth of its corrugations is small compared to a wavelength. This paper introduces a rigorous technique, the Method of Analytical Regularization (MAR), which removes these restrictions. The solution obtained by this method is free from limitations on the parameters of sinusoidal grating and possesses the capability to achieve predetermined accuracy of computations uniformly in a wide frequency band. The results of previous studies, which employed the Wiener-Hopf technique combined with a perturbation method, are compared with those obtained by the MAR; excellent concordance of results in the common parameter regimes of applicability of both methods is found. The different regimes of applicability of each approach are identified; within these, the MAR provides effective and efficient solutions to benchmark problems for testing other approximate techniques.
关键词： efficient computational algorithm in wide frequency band,scattering of E-polarized plane wave,method of analytical regularization,Floquet modes for finite grating,finite sinusoidal grating,Wiener-Hopf technique combined with perturbation method
更新于2025-09-23 15:23:52
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[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Improved Sparse Adaptive Algorithms for Accurate Non-contact Heartbeat Detection Using Time-Window-Variation Technique
摘要： Recently, a sparse adaptive algorithm termed zero-attracting sign least-mean-square (ZA-SLMS), has been clarified to be able to reconstruct robustly heartbeat spectrum by Doppler radar signal. However, since the strengths of noise evidently differ under different body motions, the sparse heartbeat spectra cannot be always acquired accurately by the constant regularization parameter (REPA) that balances the gradient correction and the sparse penalty, applying in the ZA-SLMS algorithm. In this paper, an improved ZA-SLMS algorithm is proposed by introducing adaptive REPA (AREPA), where the proportion of sparse penalty is adjusted based on the standard deviation of radar data. Moreover, to enhance the stability of heartbeat detection, a time-window-variation (TWV) technique is further introduced in the improved ZA-SLMS algorithm, considering the fact that the position of spectral peak associated with the heart rate (HR) is stable when the length of time window changes within a short period. Experimental results measured against five subjects validated that our proposal reliably improves the error of HR estimation than the standard ZA-SLMS algorithm.
关键词： heartbeat detection,time-window-variation,Doppler radar,regularization parameter,sparse adaptive algorithm
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Sparse and Smooth Feature Extraction for Hyperspectral Imagery
摘要： In this paper, a hyperspectral feature extraction (FE) method called sparse and smooth low-rank analysis (SSLRA) is proposed. First, we propose a new low-rank model for hyperspectral images (HSIs). In the new model, HSI is decomposed into smooth and sparse unknown features which live in an unknown orthogonal subspace. Then, the sparse and smooth features are simultaneously estimated using a non-convex constrained penalized cost function. In the experiments, SSLRA is applied on a real HSI and the smooth features extracted are used for the HSI classification. The results confirm improvements in classification accuracies compared to state-of-the-art FE methods.
关键词： regularization,Feature extraction,sparsity,low-rank model,total variation,hyperspectral image
更新于2025-09-23 15:22:29
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Edge-illumination x-ray phase contrast imaging restoration using discrete curvelet regularization transform
摘要： This article considers the problem of recovering edge-illumination x-ray phase contrast (EIXPC) images from a set of potentially Poisson noisy projection measurements. The authors cast a recovery as a sparse regularization problem based on Anscombe multiscale variance stabilizing transform (MS-VST) with fast discrete curvelet transform which was applied to simulated edge-illumination x-ray phase contrast images. For accurate modelling, the noise characteristics of the EIXPCi data are used to determine the relative importance of each projection. Two implementations of curvelet sparse regularization transforms were applied, including the unequally-spaced fast Fourier transform and the wrapping-based transform. The algorithms were evaluated in terms of contrast improvement, quality of image restoration, object perceptibility, and peak signal-to-noise ratio. The methods provide nearly optimal solution without excessive memory and recovery time requirement. The performance of the proposed algorithms is demonstrated through a series of complex numerical geometric and anthropomorphic phantom studies. The results of numerical simulations demonstrate that the discrete curvelet transform with MS-VST is fast and robust, and it can effectively improve image quality, preserve and enhance edges and restore lost information while signi?cantly reducing the noise. Additionally, both sparse sampling and decreasing x-ray tube current (i.e. noisy data) lead to the reduction of radiation dose in the x-ray imaging.
关键词： x-ray imaging,x-ray phase-contrast,curvelet regularization,biomedical imaging
更新于2025-09-23 15:22:29
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Space-time variant weighted regularization in compressed sensing cardiac cine MRI
摘要： Purpose: To analyze the impact on image quality and motion fidelity of a motion-weighted space-time variant regularization term in compressed sensing cardiac cine MRI. Methods: k-t SPARSE-SENSE with temporal total variation (tTV) is used as the base reconstruction algorithm. Motion in the dynamic image is estimated by means of a robust registration technique for non-rigid motion. The resulting deformation fields are used to leverage the regularization term. The results are compared with standard k-t SPARSE-SENSE with tTV regularization as well as with an improved version of this algorithm that makes use of tTV and temporal Fast Fourier Transform regularization in x-f domain. Results: the proposed method with space-time variant regularization provides higher motion fidelity and image quality than the two previously reported methods. Difference images between undersampled reconstruction and fully sampled reference images show less systematic errors with the proposed approach. Conclusions: usage of a space-time variant regularization offers reconstructions with better image quality than the state of the art approaches used for comparison.
关键词： cine cardiac MRI,space-time variant regularization,k-t SPARSE-SENSE,compressed sensing
更新于2025-09-23 15:22:29
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Nonconvex-sparsity and Nonlocal-smoothness Based Blind Hyperspectral Unmixing
摘要： Blind hyperspectral unmixing (HU), as a crucial technique for hyperspectral data exploitation, aims to decompose mixed pixels into a collection of constituent materials weighted by the corresponding fractional abundances. In recent years, nonnegative matrix factorization (NMF) based methods have become more and more popular for this task and achieved promising performance. Among these methods, two types of properties upon the abundances, namely the sparseness and the structural smoothness, have been explored and shown to be important for blind HU. However, all of previous methods ignores another important insightful property possessed by a natural hyperspectral images (HSI), non-local smoothness, which means that similar patches in a larger region of an HSI are sharing the similar smoothness structure. Based on previous attempts on other tasks, such a prior structure reflects intrinsic configurations underlying a HSI, and is thus expected to largely improve the performance of the investigated HU problem. In this paper, we firstly consider such prior in HSI by encoding it as the non-local total variation (NLTV) regularizer. Furthermore, by fully exploring the intrinsic structure of HSI, we generalize NLTV to non-local HSI TV (NLHTV) to make the model more suitable for the bind HU task. By incorporating these two regularizers, together with a non-convex log-sum form regularizer characterizing the sparseness of abundance maps, to the NMF model, we propose novel blind HU models named NLTV/NLHTV and log-sum regularized NMF (NLTV-LSRNMF/NLHTV-LSRNMF), respectively. To solve the proposed models, an efficient algorithm is designed based on alternative optimization strategy (AOS) and alternating direction method of multipliers (ADMM). Extensive experiments conducted on both simulated and real hyperspectral data sets substantiate the superiority of the proposed approach over other competing ones for blind HU task.
关键词： log-sum penalty,non-negative matrix factorization,non-local total variation regularization,blind unmixing,Hyperspectral imaging
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Multichannel Sliding Spotlight SAR Imaging Based on Sparse Signal Processing
摘要： Multi-channel sliding spotlight SAR can achieve high-resolution and wide-swath imaging. The sparse reconstruction algorithm can improve the quality of imaging. By applying the sparse reconstruction algorithm to multi-channel sliding spotlight SAR imaging, azimuth ambiguities, noise and clutter can be suppressed effectively. In this paper, 1 regularization based multi-channel sliding spotlight SAR imaging method is proposed. The proposed method combines the DPCA imaging operators with the 1 regularization scheme to solve the nonuniform sampling and azimuth ambiguities problem. The proposed method can suppress azimuth ambiguities more effectively than the reconstruction filter algorithm based DPCA technology in the case of a lower PRF. The experiment results verify the effectiveness of the proposed method.
关键词： DPCA,Multi-channel,1 regularization,sliding spotlight SAR
更新于2025-09-23 15:22:29
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Maximum entropy based non-negative optoacoustic tomographic image reconstruction
摘要： Objective: Optoacoustic (photoacoustic) tomography is aimed at reconstructing maps of the initial pressure rise induced by the absorption of light pulses in tissue. In practice, due to inaccurate assumptions in the forward model, noise and other experimental factors, the images are often afflicted by artifacts, occasionally manifested as negative values. The aim of the work is to develop an inversion method which reduces the occurrence of negative values and improves the quantitative performance of optoacoustic imaging. Methods: We present a novel method for optoacoustic tomography based on an entropy maximization algorithm, which uses logarithmic regularization for attaining non-negative reconstructions. The reconstruction image quality is further improved using structural prior based fluence correction. Results: We report the performance achieved by the entropy maximization scheme on numerical simulation, experimental phantoms and in-vivo samples. Conclusion: The proposed algorithm demonstrates superior reconstruction performance by delivering non-negative pixel values with no visible distortion of anatomical structures. Significance: Our method can enable quantitative optoacoustic imaging, and has the potential to improve pre-clinical and translational imaging applications.
关键词： inverse problems,image reconstruction,Optical parameters,regularization theory,photoacoustic tomography
更新于2025-09-23 15:22:29