Article
Engineering, Electrical & Electronic
Yun-Bin Zhao, Zhi-Quan Luo
Summary: The optimal k-thresholding and optimal k-thresholding pursuit are introduced as frameworks for compressed sensing and signal approximation, leading to the development of efficient algorithms for signal reconstruction. While initial results show stability in signal reconstruction across various sparsity levels, the guaranteed performance for parameters omega >= 2 has yet to be established. This study aims to demonstrate the guaranteed performance of these techniques and establish the first performance results for relaxed optimal k-thresholding and pursuit with omega >= 2, while also providing a numerical comparison with existing methods.
Article
Optics
Yanshan Fan, Miaoqing Bai, Shuxiao Wu, Zhixing Qiao, Jianyong Hu
Summary: This paper proposes an efficient single-photon compressed sensing imaging scheme, which uses a new mask design and optimizes the number of masks to achieve high-quality imaging. The imaging speed and quality are greatly improved compared with the commonly used Hadamard scheme.
Article
Mathematics, Applied
Liaoyuan Zeng, Peiran Yu, Ting Kei Pong
Summary: The paper explores the use of the l(1)/l(2) norm ratio in compressed sensing problems, proposes an algorithm for noise situations, and demonstrates through numerical experiments that the algorithm can recover the original sparse vectors with reasonable accuracy.
SIAM JOURNAL ON OPTIMIZATION
(2021)
Article
Remote Sensing
Gongwei Xiao, Genyou Liu, Jikun Ou, Guolin Liu, Shengliang Wang, Jiachen Wang, Ming Gao
Summary: This study discusses key issues in the processing of global tropospheric grid data, presents the use of compressed sensing for sparse reconstruction, and proposes the mini-batch K-SVD algorithm to speed up calculations. Experimental results demonstrate that compressed sensing yields more accurate solutions than traditional spherical harmonic expansion, saving real-time transmission costs and enabling data encryption and compression.
Article
Engineering, Electrical & Electronic
Razieh Torkamani, Hadi Zayyani, Ramazan Ali Sadeghzadeh
Summary: This paper proposes a novel model-based distributed compressive sensing algorithm that exploits inter-signal correlations and can recover multiple sparse signals simultaneously. The algorithm uses a Bayesian decentralized approach and a joint sparsity model to utilize signal structures effectively.
SIGNAL PROCESSING-IMAGE COMMUNICATION
(2021)
Article
Pediatrics
Dianna M. E. Bardo, Nicholas Rubert
Summary: MRI is often ideal for imaging children of any age and can produce high-resolution images, characterize tissues, and detect physiological states. Selecting appropriate imaging sequence parameters can affect both the quality of the imaged tissue and the acquisition time.
PEDIATRIC RADIOLOGY
(2022)
Article
Mathematics, Applied
Paul Breiding, Fulvio Gesmundo, Mateusz Michalek, Nick Vannieuwenhoven
Summary: This paper introduces the broad subclass of algebraic compressed sensing problems, where structured signals are modeled either explicitly or implicitly via polynomials, including low-rank matrix and tensor recovery. Powerful techniques from algebraic geometry are employed to study well-posedness of general compressed sensing problems, including existence, local recoverability, global uniqueness, and local smoothness. The main results are summarized in thirteen questions and answers in algebraic compressed sensing, with most of the answers being optimal and dependent only on the dimension of the model.
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS
(2023)
Article
Computer Science, Artificial Intelligence
Giovanni S. Alberti, Paolo Campodonico, Matteo Santacesaria
Summary: Photoacoustic tomography (PAT) is a new imaging modality that aims to measure the high-contrast optical properties of tissues through high-resolution ultrasonic measurements. Recent research has focused on applying compressed sensing to reduce measuring times while maintaining reconstruction quality. In many practical measurement setups, compressed sensing PAT simplifies to compressed sensing for undersampled Fourier measurements, which has been validated through extensive numerical simulations.
SIAM JOURNAL ON IMAGING SCIENCES
(2021)
Article
Engineering, Multidisciplinary
Ondrej Kovac, Jozef Kromka, Jan Saliga, Antonia Juskova
Summary: This paper presents a novel multiwavelet-based hybrid method for compressed sensing and reconstruction of ECG signals. The method removes low-frequency components using multi-scaling wavelet functions and acquires high-frequency components using a new compressed sensing acquisition approach. The results demonstrate good reconstruction quality and high compression ratio.
Article
Engineering, Electrical & Electronic
Yong Dong, Ya-Nan Yang, Abul Kalam Azad, Zengsen Yang, Kuanglu Yu, Shuang Zhao
Summary: In this paper, a compressed sensing (CS) method based on the K-Singular value decomposition (K-SVD) algorithm is proposed for the accurate extraction of sensing information from Brillouin optical fiber distributed sensors. The experimental results show that this method can effectively reconstruct experimental signals with reduced data and provides satisfactory measurement accuracy.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Weizhi Lu, Mingrui Chen, Kai Guo, Weiyu Li
Summary: For deep networks with complex nonlinearity, this paper proposes understanding and constructing them as a cascade of compressed sensing. Each compressed sensing module consists of two layers, which correspond to the two data transforms involved in compressed sensing. The proposed construction has the advantages of being analyzable with compressed sensing theory and enabling layerwise learning via back-propagating the target.
IEEE SIGNAL PROCESSING LETTERS
(2023)
Article
Computer Science, Information Systems
Jun Wang
Summary: In this paper, a wonderful triangle is introduced to explore the concrete metric relationship between llxll1/llxll and llxll0. Based on the analysis of the iterative soft-thresholding operator, the angle of the triangle corresponding to the side llxll. - llxll1/llxll0 is studied, demonstrating the meaningfulness of signal sparsity within a certain exact interval.
INFORMATION SCIENCES
(2022)
Article
Telecommunications
Xinhua Jiang, Ning Li, Yan Guo, Jie Liu, Cong Wang
Summary: In this paper, a sensing matrix optimization method based on Compressed Sensing (CS) for multi-target localization is proposed. By considering optimization under the t%-averaged mutual coherence, a hybrid metaheuristic algorithm named Genetic Algorithm-Tabu Local Search (GA-TLS) is used to address the combinatorial optimization problem. Simulation results show that the proposed method leads to much less localization error compared to traditional methods.
CHINA COMMUNICATIONS
(2022)
Article
Biotechnology & Applied Microbiology
Brian Cleary, Brooke Simonton, Jon Bezney, Evan Murray, Shahul Alam, Anubhav Sinha, Ehsan Habibi, Jamie Marshall, Eric S. Lander, Fei Chen, Aviv Regev
Summary: Recent method CISI leverages gene expression patterns to reduce imaging cycles and achieve spatially resolved gene expression maps more efficiently. Applying CISI to mouse brain sections accurately recovered spatial abundance of multiple individual genes.
NATURE BIOTECHNOLOGY
(2021)
Article
Engineering, Electrical & Electronic
Vicky Kouni, Yannis Panagakis
Summary: We propose a new deep unfolding network for analysis-sparsity-based Compressed Sensing, called Decoding Network (DECONET), which jointly learns a decoder and a redundant sparsifying analysis operator shared across different layers. We estimate the Rademacher complexity of DECONET and provide meaningful upper bounds for its generalization error. Experimental results on synthetic and real-world datasets confirm the superiority of DECONET over state-of-the-art unfolding networks, consistent with our theoretical findings.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2023)