Relevant bibliographies by topics / Synergistic regularization

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Academic literature on the topic 'Synergistic regularization'

Author: Grafiati
Published: 29 March 2025
Last updated: 31 July 2025

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Journal articles on the topic "Synergistic regularization"

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Cueva, Evelyn, Alexander Meaney, Samuli Siltanen, and Matthias J. Ehrhardt. "Synergistic multi-spectral CT reconstruction with directional total variation." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2204 (2021): 20200198. http://dx.doi.org/10.1098/rsta.2020.0198.

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This work considers synergistic multi-spectral CT reconstruction where information from all available energy channels is combined to improve the reconstruction of each individual channel. We propose to fuse these available data (represented by a single sinogram) to obtain a polyenergetic image which keeps structural information shared by the energy channels with increased signal-to-noise ratio. This new image is used as prior information during a channel-by-channel minimization process through the directional total variation. We analyse the use of directional total variation within variational
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Mehranian, Abolfazl, Martin A. Belzunce, Claudia Prieto, Alexander Hammers, and Andrew J. Reader. "Synergistic PET and SENSE MR Image Reconstruction Using Joint Sparsity Regularization." IEEE Transactions on Medical Imaging 37, no. 1 (2018): 20–34. http://dx.doi.org/10.1109/tmi.2017.2691044.

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Cui, Xinye, Houpu Li, Yanting Yu, Shaofeng Bian, and Guojun Zhai. "A Hybrid Dropout Method for High-Precision Seafloor Topography Reconstruction and Uncertainty Quantification." Applied Sciences 15, no. 11 (2025): 6113. https://doi.org/10.3390/app15116113.

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Seafloor topography super-resolution reconstruction is critical for marine resource exploration, geological monitoring, and navigation safety. However, sparse acoustic data frequently result in the loss of high-frequency details, and traditional deep learning models exhibit limitations in uncertainty quantification, impeding their practical application. To address these challenges, this study systematically investigates the combined effects of various regularization strategies and uncertainty quantification modules. It proposes a hybrid dropout model that jointly optimizes high-precision recon
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Perelli, Alessandro, and Martin S. Andersen. "Regularization by denoising sub-sampled Newton method for spectral CT multi-material decomposition." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2200 (2021): 20200191. http://dx.doi.org/10.1098/rsta.2020.0191.

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Spectral Computed Tomography (CT) is an emerging technology that enables us to estimate the concentration of basis materials within a scanned object by exploiting different photon energy spectra. In this work, we aim at efficiently solving a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT. In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function using a randomized second order method. By approximating the Newton step using a sketching of the Hessian of the likelihood function,
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Jørgensen, J. S., E. Ametova, G. Burca, et al. "Core Imaging Library - Part I: a versatile Python framework for tomographic imaging." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2204 (2021): 20200192. http://dx.doi.org/10.1098/rsta.2020.0192.

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We present the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional filtered back-projection reconstruction tends to be insufficient for highly noisy, incomplete, non-standard or multi-channel data arising for example in dynamic, spectral and in situ tomography. CIL provides an extensive modular optimization framework for prototyping reconstruction methods including sparsity and total variation regularization, as well as tools for loading, preprocessing and visualizing tomographic da
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Bahadur, Rabya, Saeed ur Rehman, Ghulam Rasool, and Muhammad AU Khan. "Synergy Estimation Method for Simultaneous Activation of Multiple DOFs Using Surface EMG Signals." NUST Journal of Engineering Sciences 14, no. 2 (2022): 66–73. http://dx.doi.org/10.24949/njes.v14i2.661.

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Surface electromyography signals are routinely used for designing prosthetic control systems. The concept of synergy estimation for muscle control interpretation is being explored extensively. Synergies estimated for a single active degree of freedom (DoF) are found to be uncorrelated and provide better results when used for single movement classification; however, an increase of simultaneously active DoFs leads to complex limb movements and multiple DoF detection becomes a challenge. Synergy estimation is a non-convex optimization technique, to provide better estimation this paper proposes th
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Zhong, Lihua, Tong Ye, Yuyao Yang, et al. "Deep Reinforcement Learning-Based Joint Low-Carbon Optimization for User-Side Shared Energy Storage–Distribution Networks." Processes 12, no. 9 (2024): 1791. http://dx.doi.org/10.3390/pr12091791.

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As global energy demand rises and climate change poses an increasing threat, the development of sustainable, low-carbon energy solutions has become imperative. This study focuses on optimizing shared energy storage (SES) and distribution networks (DNs) using deep reinforcement learning (DRL) techniques to enhance operation and decision-making capability. An innovative dynamic carbon intensity calculation method is proposed, which more accurately calculates indirect carbon emissions of the power system through network topology in both spatial and temporal dimensions, thereby refining carbon res
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Du, Lehui, Baolin Qu, Fang Liu, et al. "Precise prediction of the radiation pneumonitis with RPI: An explorative preliminary mathematical model using genotype information." Journal of Clinical Oncology 37, no. 15_suppl (2019): e14569-e14569. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e14569.

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e14569 Background: Radiation pneumonitis (RP) is the most significant dose-limiting toxicity and is one major obstacle for the radiotherapy of lung cancer. Reliable predictive factors or methods are strongly demanded by radiation oncologists. The purpose of this study is by determining the effectiveness of both genetic and non-genetic factors on their impact on the development of RP, to develop a clinically practicable approach for the risk assessment of RP. Methods: One hundred eighteen lung cancer patients who received radiotherapy were enrolled. RP events were prospectively scored using the
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Di Sciacca, G., L. Di Sieno, A. Farina, et al. "Enhanced diffuse optical tomographic reconstruction using concurrent ultrasound information." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2204 (2021): 20200195. http://dx.doi.org/10.1098/rsta.2020.0195.

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Multimodal imaging is an active branch of research as it has the potential to improve common medical imaging techniques. Diffuse optical tomography (DOT) is an example of a low resolution, functional imaging modality that typically has very low resolution due to the ill-posedness of its underlying inverse problem. Combining the functional information of DOT with a high resolution structural imaging modality has been studied widely. In particular, the combination of DOT with ultrasound (US) could serve as a useful tool for clinicians for the formulation of accurate diagnosis of breast lesions.
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Lu, Yifan, Ziqi Zhang, Chunfeng Yuan, et al. "Set Prediction Guided by Semantic Concepts for Diverse Video Captioning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3909–17. http://dx.doi.org/10.1609/aaai.v38i4.28183.

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Diverse video captioning aims to generate a set of sentences to describe the given video in various aspects. Mainstream methods are trained with independent pairs of a video and a caption from its ground-truth set without exploiting the intra-set relationship, resulting in low diversity of generated captions. Different from them, we formulate diverse captioning into a semantic-concept-guided set prediction (SCG-SP) problem by fitting the predicted caption set to the ground-truth set, where the set-level relationship is fully captured. Specifically, our set prediction consists of two synergisti
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Dissertations / Theses on the topic "Synergistic regularization"

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Wang, Zhihan. "Reconstruction des images médicales de tomodensitométrie spectrale par apprentissage profond." Electronic Thesis or Diss., Brest, 2024. http://www.theses.fr/2024BRES0124.

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La tomodensitométrie se concentre sur deux sujets clés : la réduction de la dose de radiation et l’imagerie multi-énergétique, qui sont interconnectés. La tomodensitométrie spectrale, une avancée émergente, capture des données sur plusieurs énergies de rayons X pour mieux distinguer les matériaux, minimisant le besoin de scans répétés et ainsi réduisant l’exposition globale aux radiations.Cependant, la réduction du nombre de photons dans chaque bin d’énergie rend les méthodes de reconstruction traditionnelles sensibles au bruit. Ainsi, l’apprentissage profond, qui a montré un potentiel considé
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