A5101842901- Sparse and Compressive Sensing Techniques
- Image and Signal Denoising Methods
- Advanced Image Processing Techniques
- Photoacoustic and Ultrasonic Imaging
- Photoreceptor and optogenetics research
- Thin-Film Transistor Technologies
- Sleep and Wakefulness Research
- Organic Light-Emitting Diodes Research
- Antenna Design and Analysis
- Image Processing Techniques and Applications
- Receptor Mechanisms and Signaling
- Video Coding and Compression Technologies
- Image and Video Quality Assessment
- Engineering Education and Curriculum Development
- Advanced Data Compression Techniques
- Risk and Safety Analysis
- Advanced biosensing and bioanalysis techniques
- Advanced Vision and Imaging
- RFID technology advancements
- Nicotinic Acetylcholine Receptors Study
- Image Enhancement Techniques
- Human-Automation Interaction and Safety
- Occupational Health and Safety Research
- Photopolymerization techniques and applications
- Amino Acid Enzymes and Metabolism
Chinese Institute for Brain Research
2022-2024
Eindhoven University of Technology
2024
Northwestern Polytechnical University
2022-2024
National University of Defense Technology
2021-2023
Tencent (China)
2017-2023
Shenzhen University
2021-2023
BOE Technology Group (China)
2020-2023
Wuhan University
2022
Ministry of Industry and Information Technology
2022
Harbin Institute of Technology
2014-2018
Gaussian random matrix (GRM) has been widely used to generate linear measurements in compressive sensing (CS) of natural images. However, practice, there actually exist two problems with GRM. One is that GRM non-sparse and complicated, leading high computational complexity difficulty hardware implementation. The other regardless the characteristics signal generated by are also random, which results low efficiency compression coding. In this paper, we design a novel local structural...
Most multi-exposure image fusion (MEF) methods perform unidirectional alignment within limited and local regions, which ignore the effects of augmented locations preserve deficient global features. In this work, we propose a multi-scale bidirectional network via deformable self-attention to adaptive fusion. The proposed exploits differently exposed images aligns them normal exposure in varying degrees. Specifically, design novel module that considers variant long-distance attention...
Immunodetection methods based on antibody-antigen interactions are routinely used in biological and clinical laboratories. DNA-labeled antibodies offer the advantage of simultaneous detection multiple target molecules via orthogonal DNA barcode sequences. However, current for antibody-DNA conjugation typically involve non-site-specific modifications tailored each specific application, a process that is labor-intensive often compromises antibody affinity specificity. To overcome these...
Gaussian random matrix (GRM) has been widely used to generate linear measurements in compressed sensing (CS) of natural images. However, there actually exist two disadvantages with GRM practice. One is that large memory requirement and high computational complexity, which restrict the applications CS. Another CS randomly obtained by cannot provide sufficient reconstruction performances. In this paper, a Deep neural network based Sparse Measurement Matrix (DSMM) learned proposed convolutional...
Manipulating and real-time monitoring of neuronal activities with cell-type specificity precise spatiotemporal resolution during animal behavior are fundamental technologies for exploring the functional connectivity, information transmission, physiological functions neural circuits<italic>in vivo</italic>. However, current techniques optogenetic stimulation activity recording mostly operate independently. Here, we report an all-fiber-transmission photometry system simultaneous manipulation...
Traditional image compressed sensing (CS) coding frameworks solve an inverse problem that is based on the measurement tools (prediction, quantization, entropy coding, etc.) and optimization reconstruction method. These CS face challenges of improving efficiency at encoder, while also suffering from high computational complexity decoder. In this paper, we move forward a step propose novel deep network framework natural images, which consists three sub-networks: sampling sub-network, offset...
Existing image compressed sensing (CS) coding frameworks usually solve an inverse problem based on measurement and optimization-based reconstruction, which still exist the following two challenges: (1) widely used random sampling matrix, such as Gaussian Random Matrix (GRM), leads to low efficiency, (2) reconstruction methods generally maintain a much higher computational complexity. In this article, we propose new convolutional neural network CS framework using local structural (dubbed...
The compressed sensing (CS) has been successfully applied to image compression in the past few years as most signals are sparse a certain domain. Several CS reconstruction models have proposed and obtained superior performance. However, these methods suffer from blocking artifacts or ringing effects at low sampling ratios cases. To address this problem, we propose deep convolutional Laplacian Pyramid Compressed Sensing Network (LapC-SNet) for CS, which consists of sub-network sub-network. In...
The development of healthcare industry, especially Internet Medical Things (IoMT), has generated considerable unlabeled electrocardiogram (ECG) signals. This article proposes a new unsupervised feature learning method for these 12-lead ECGs, type 12-channel 1-D time series. Based on contrastive predictive coding (CPC), it considers the characteristics ECGs and develops novel lead-separation CPC (LSCPC) lead-combination (LCCPC). Specifically, LSCPC captures intralead features each lead, while...
In this paper, the hierarchical frame based video compressed sensing (CS) framework is proposed, which outperforms traditional through better exploitation of frames correlation with reference frames, unequal sample subrates setting among in different layers and reduction error propagation. By considering spatial temporal correlations sequence, a spatial-temporal sparse representation recovery proposed for framework. The similar blocks both current these recovered are composed as group,...
Currently, it is indicated that terahertz irradiation capable of permeabilizing cell membrane by electroporation, which able to facilitate transmembrane transport molecules and ions for medicine clinical applications. This paper numerically studies the changes in permeability owing hydrophilic pores electroporation response picosecond pulse train. The capacitive current ion channel currents are taken into account simulation conductance pores. numerical results reveal increases firstly at...
Transformer-based image restoration methods in adverse weather have achieved significant progress. Most of them use self-attention along the channel dimension or within spatially fixed-range blocks to reduce computational load. However, such a compromise results limitations capturing long-range spatial features. Inspired by observation that weather-induced degradation factors mainly cause similar occlusion and brightness, this work, we propose an efficient Histogram Transformer (Histoformer)...
Although conventional fluorescence intensity imaging can be used to qualitatively study the drug toxicity of nanodrug carrier systems at single-cell level, it has limitations for studying transport across membranes. Fluorescence correlation spectroscopy (FCS) provide quantitative information on concentration and diffusion in a small area cell membrane; thus, is an ideal tool membrane. In this paper, FCS method was measure coefficients concentrations carbon dots (CDs), doxorubicin (DOX)...
Video quality in real-time mobile communication is often influenced by the ambient light. In low-lighting condition, videos are usually dark and lack details. To solve above problem, we propose a fast video enhancement algorithm exploiting constrained spatial-temporal model, which three terms including luminance accuracy, contrast accuracy temporal consistency all considered. For first term, average level of each frame taken into account set off-line trained functions. second adjustment...