20 papers · ranked by Valyu relevance
Vladislav Kravets, Adrian Stern
Compressive sensing (CS) is a sub-Nyquist sampling framework that has been employed to improve the performance of numerous imaging applications during the last 15 years. Yet, its application for large and high-resolution imaging remains challenging in terms of the computation and acquisition effort involved. Often…
Rens Baeyens, Joachim Denil, Jan Steckel, Dennis Laurijssen + 2 more
'Walter Daems' 'Jari Nurmi'] In this paper, a model-based firmware generator is presented towards complex sampling schemes. The framework is capable of automatically generating a fixed-rate Shannon-compliant acquisition scheme, as well as a variable-rate compressive sensing acquisition scheme. The generation starts…
Olivier Lim, Stéphane Mancini, Mauro Dalla Mura, Francesca Cigna + 1 more
'Antonio Iodice'] Hyperspectral imaging has been attracting considerable interest as it provides spectrally rich acquisitions useful in several applications, such as remote sensing, agriculture, astronomy, geology and medicine. Hyperspectral devices based on compressive acquisitions have appeared recently as an…
Yiming Zeng, Shahin Khobahi, Mojtaba Soltanalian
—One-bit compressive sensing is concerned with the accurate recovery of an underlying sparse signal of interest from its one-bit noisy measurements. The conventional signal recovery approaches for this problem are mainly developed based on the assumption that an exact knowledge of the sensing matrix is available. In…
Kiri Choi, Won Kyu Kim, Changbong Hyeon
The putative dimension of a space spanned by chemical stimuli is deemed enormous; however, when odorant molecules are bound to a finite number of receptor types and their information is transmitted and projected to a perceptual odor space in the brain, a substantial reduction in dimensionality is made. Compressed…
Qu, Gang, Wang, Ping + 4 more
Deep networks have achieved remarkable success in image compressed sensing (CS) task, namely reconstructing a high-fidelity image from its compressed measurement. However, existing works are deficient in incoherent compressed measurement at sensing phase and implicit measurement representations at reconstruction phase…
Chenxi Qiu, Peng Wang, Xiangshun Kong, Feng Yan + 4 more
'Tao Yue' 'Xuemei Hu' 'Lei Huang'] Single-photon avalanche diodes (SPADs) are novel image sensors that record photons at extremely high sensitivity. To reduce both the required sensor area for readout circuits and the data throughput for SPAD array, in this paper, we propose a snapshot compressive sensing single-photon…
Youhao Yu, Richard M. Dansereau
- Compressive sensing (CS) is a technique that enables the recovery of sparse signals using fewer measurements than traditional sampling methods. To address the computational challenges of CS reconstruction, our objective is to develop an interpretable and concise neural network model for reconstructing natural images…
Jinn Ho, Wen-Liang Hwang
Compressive sensing involves the inversion of a mapping SD ∈ R m × n , where m < n , S is a sensing matrix, and D is a sparisfying dictionary. The restricted isometry property is a powerful sufficient condition for the inversion that guarantees the recovery of highdimensional sparse vectors from their low-dimensional…
Benjamin W. Roop, Benjamin Parrell, Adam C. Lammert
Uncovering cognitive representations is an elusive goal that is increasingly pursued using the reverse correlation method, wherein human subjects make judgments about ambiguous stimuli. Employing reverse correlation often entails collecting thousands of stimulus-response pairs, which severely limits the breadth of…
Jane A. Gargano, Abigail Rice, Divya A. Chari, Benjamin Parrell + 1 more
Reverse correlation is a widely-used and well-established method for probing latent perceptual representations in which subjects render subjective preference responses to ambiguous stimuli. Stimuli are purposefully designed to have no direct relationship with the target representation (e.g., they are…
Nicholas Dwork, Peder E. Z. Larson
The discrete curvelet transform decomposes an image into a set of fundamental components that are distinguished by direction and size as well as a low-frequency representation. The curvelet representation is approximately sparse; thus, it is a useful sparsifying transformation to be used with compressed sensing.…
George Kourousias, Fulvio Billè, Matteo Ippoliti, Francesco Guzzi + 2 more
Scanning microscopies and spectroscopies like X-ray Fluorescence (XRF), Scanning Transmission X-ray Microscopy (STXM), and Ptychography are of very high scientific importance as they can be employed in several research fields. Methodology and technology advances aim at analysing larger samples at better resolution…
Phillip Navarro, Karim Oweiss
Mapping functional connectivity between neurons is an essential step towards probing the neural computations mediating behavior. The ability to consistently and robustly determine synaptic connectivity maps in large populations of interconnected neurons is a significant challenge in terms of yield, accuracy and…
Piyush Raj, Lintong Wu, Jeong Hee Kim, Raj Bhatt + 2 more
Not to Acquire: Evaluating Compressive Sensing for Raman Spectroscopy in Biology Authors: Piyush Raj, Lintong Wu, Jeong Hee Kim, Raj Bhatt, Kristine Glunde, Ishan Barman Raman spectroscopy has revolutionized the field of chemical biology by providing detailed chemical and compositional information with minimal sample…
Gang Qu, Ping Wang, Siming Zheng, Xin Yuan
We propose a deep probabilistic unfolding model to address the classical quantized compressive sensing problem that leverages an unfolding framework to enhance the reconstruction accuracy and efficiency. Unlike previous unfolding methods that apply L2 projection to measurements, we derive a closed-form, numerically…
Ajay Gunalan, Marco Castello, Simonluca Piazza, Shunlei Li + 3 more
'Alberto Diaspro' 'Leonardo S. Mattos' 'Paolo Bianchini'] Abstract—we present a novel approach to implement compressive sensing in laser scanning microscopes (LSM), specifically in image scanning microscopy (ISM), using a single-photon avalanche diode (SPAD) array detector. Our method addresses two significant…
Jon Alvarez Justo, Milica Orlandić
—Hyperspectral Imaging (HSI) is used in a wide range of applications such as remote sensing, yet the transmission of the HS images by communication data links becomes challenging due to the large number of spectral bands that the HS images contain together with the limited data bandwidth available in real applications.…
Pumiao Yan, Dante G. Muratore, E.J. Chichilnisky, Boris Murmann + 1 more
Scaling neural recording systems to thousands of channels creates extreme bandwidth demands, posing a challenge for resource-constrained, implantable devices. This work introduces an adaptive, multi-stage compression framework for high-bandwidth neural interfaces. The system combines a Wired-OR analog-to-digital…
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This study presents the use of laser-driven microbubbles for micro-patterning Ti3C2TX MXenes on flexible polyethylene terephthalate films, yielding conductive micropatterns without the need for pre- or post-processing. Characterization of the electrical properties under varying strain conditions revealed distinct…