18 citations · 37 across the 14 of their papers we have counts for
15 papers
FoCal: Frequency-Oriented Cross-Modal Interaction and Spectral Calibration for Aerial Visible-Infrared Object Detection
Ben Liang, Chao Sui, Junqi Bai +4
In aerial RGB--IR object detection, effectively exploiting complementary information across modalities is critical for robust perception under complex illumination and environmenta…
3DTMDet: A Dual-Path Synergy Network of Transformer and SSM for 3D Object Detection in Point Clouds
Bingwen Qiu, Yuan Liu, Junqi Bai +5
A fundamental challenge in point cloud object detection lies in the conflict between the extreme sparsity of distant points and the need for remote context understanding. The exist…
FSCM: Frequency-Enhanced Spatial-Spectral Coupled Mamba for Infrared Hyperspectral Image Colorization
Tingting Liu, Yuan Liu, Guiping Chen +2
Thermal infrared imaging is robust to illumination variations and smoke interference, making it important for all-weather perception. However, the lack of natural color and fine te…
FMC-DETR: Frequency-Decoupled Multi-Domain Coordination for Aerial-View Object Detection
Ben Liang, Hongguang Wei, Yuan Liu +4
Remote sensing object detection is a critical technology for real-world applications such as natural resource monitoring, traffic management, and UAV-based rescue. Detecting tiny o…
MTSIC: Multi-stage Transformer-based GAN for Spectral Infrared Image Colorization
Tingting Liu, Yuan Liu, Jinhui Tang +5
Thermal infrared (TIR) images, acquired through thermal radiation imaging, are unaffected by variations in lighting conditions and atmospheric haze. However, TIR images inherently…
Imaging through multimode fibres with physical prior
Chuncheng Zhang, Yingjie Shi, Zheyi Yao +2
Imaging through perturbed multimode fibres based on deep learning has been widely researched. However, existing methods mainly use target-speckle pairs in different configurations.…
ITRE: Low-light Image Enhancement Based on Illumination Transmission Ratio Estimation
Yu Wang, Yihong Wang, Tong Liu +2
Noise, artifacts, and over-exposure are significant challenges in the field of low-light image enhancement. Existing methods often struggle to address these issues simultaneously.…
Super-resolution imaging through a multimode fiber: the physical upsampling of speckle-driven
Chuncheng Zhang, Tingting Liu, Zhihua Xie +4
Following recent advancements in multimode fiber (MMF), miniaturization of imaging endoscopes has proven crucial for minimally invasive surgery in vivo. Recent progress enabled by…
Hyperspectral Image Super-Resolution via Dual-domain Network Based on Hybrid Convolution
Tingting Liu, Yuan Liu, Chuncheng Zhang +3
Since the number of incident energies is limited, it is difficult to directly acquire hyperspectral images (HSI) with high spatial resolution. Considering the high dimensionality a…
Dynamic mutation enhanced greedy strategy for wavefront shaping
Chuncheng Zhang, Xiubao Sui, Zheyi Yao +5
Optical focusing through scattering media has important implications for optical applications in medicine, communications, and detection. In recent years, many wavefront shaping me…
Gradient Domain Weighted Guided Image Filtering
Bo Wang, Yihong Wang, Xiubao Sui +2
Guided image filter is a well-known local filter in image processing. However, the presence of halo artifacts is a common issue associated with this type of filter. This paper prop…
Fast optical refocusing through multimode fiber bend using Cake-Cutting Hadamard encoding algorithm to improve robustness
Chuncheng Zhang, Zheyi Yao, Zhengyue Qin +5
Multimode fibres offer the advantages of high resolution and miniaturization over single mode fibers in the field of optical imaging. However, multimode fibre's imaging is suscepti…
High speed and reconfigurable optronic neural network with digital nonlinear activation
Qiuhao Wu, Jia Liu, Xiubao Sui +2
With its unique parallel processing capability, optical neural network has shown low-power consumption in image recognition and speech processing. At present, the manufacturing tec…
Object Tracking Using Spatio-Temporal Future Prediction
Yuan Liu, Ruoteng Li, Robby T. Tan +2
Occlusion is a long-standing problem that causes many modern tracking methods to be erroneous. In this paper, we address the occlusion problem by exploiting the current and future…
Thermal Infrared Colorization via Conditional Generative Adversarial Network
Xiaodong Kuang, Xiubao Sui, Chengwei Liu +3
Transforming a thermal infrared image into a realistic RGB image is a challenging task. In this paper we propose a deep learning method to bridge this gap. We propose learning the…