Results 11 to 20 of about 3,276,837 (200)
On-Chip Compressive Sensing with a Single-Photon Avalanche Diode Array [PDF]
Sensors, 2023 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 ...
Chenxi Qiu +6 more
doaj +2 more sources
Compressive Sensing DNA Microarrays [PDF]
EURASIP Journal on Bioinformatics and Systems Biology, 2009 Compressive sensing microarrays (CSMs) are DNA-based sensors that operate using group testing and compressive sensing (CS) principles. In contrast to conventional DNA microarrays, in which each genetic sensor is designed to respond to a single target, in a CSM, each sensor responds to a set of targets.
Dai, W +3 more
openaire +7 more sources
Deep learning for video compressive sensing
APL Photonics, 2020 We investigate deep learning for video compressive sensing within the scope of snapshot compressive imaging (SCI). In video SCI, multiple high-speed frames are modulated by different coding patterns and then a low-speed detector captures the integration ...
Mu Qiao, Ziyi Meng, Jiawei Ma, Xin Yuan
doaj +2 more sources
Optimization-Inspired Cross-Attention Transformer for Compressive Sensing [PDF]
Computer Vision and Pattern Recognition, 2023 By integrating certain optimization solvers with deep neural networks, deep unfolding network (DUN) with good interpretability and high performance has attracted growing attention in compressive sensing (CS).
Jie Song +4 more
semanticscholar +1 more source
COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensing [PDF]
IEEE Transactions on Image Processing, 2021 Recent deep network-based compressive sensing (CS) methods have achieved great success. However, most of them regard different sampling matrices as different independent tasks and need to train a specific model for each target sampling matrix.
Di You +4 more
semanticscholar +1 more source
ISTA-NET++: Flexible Deep Unfolding Network for Compressive Sensing [PDF]
IEEE International Conference on Multimedia and Expo, 2021 While deep neural networks have achieved impressive success in image compressive sensing (CS), most of them lack flexibility when dealing with multi-ratio tasks and multi-scene images in practical applications.
Di You, Jingfen Xie, Jian Zhang
semanticscholar +1 more source
Memory-Augmented Deep Unfolding Network for Compressive Sensing [PDF]
ACM Multimedia, 2021 Mapping a truncated optimization method into a deep neural network, deep unfolding network (DUN) has attracted growing attention in compressive sensing (CS) due to its good interpretability and high performance.
Jie Song, Bin Chen, Jian Zhang
semanticscholar +1 more source
CSformer: Bridging Convolution and Transformer for Compressive Sensing [PDF]
IEEE Transactions on Image Processing, 2021 Convolutional Neural Networks (CNNs) dominate image processing but suffer from local inductive bias, which is addressed by the transformer framework with its inherent ability to capture global context through self-attention mechanisms.
Dongjie Ye +5 more
semanticscholar +1 more source
Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks
IEEE Access, 2022 Compressive sensing allows the reconstruction of original signals from a much smaller number of samples as compared to the Nyquist sampling rate. The effectiveness of compressive sensing motivated the researchers for its deployment in a variety of ...
Irfan Ahmed +3 more
doaj +1 more source
International Journal of Engineering Technologies and Management Research, 2020 Compressive sensing is a relatively new technique in the signal processing field which allows acquiring signals while taking few samples. It works on two principles: sparsity, which pertains to the signals of interest, and incoherence, which pertains to the sensing modality.
Karl-Dirk Kammeyer +3 more
openaire +3 more sources