3 citations · 4 across the 14 of their papers we have counts for
14 papers
VEDAL: Variational Error-Driven Asynchronous Learning for 3D Gaussian Splatting Pruning
Aoduo Li, Jiancheng Li, Huan Ye +5
3D Gaussian Splatting (3DGS) achieves remarkable novel view synthesis quality with real-time rendering, yet suffers from excessive memory consumption due to millions of Gaussian pr…
ATRIE: Adaptive Tuning for Robust Inference and Emotion in Persona-Driven Speech Synthesis
Aoduo Li, Haoran Lv, Hongjian Xu +5
High-fidelity character voice synthesis is a cornerstone of immersive multimedia applications, particularly for interacting with anime avatars and digital humans. However, existing…
ProMist-5K: A Comprehensive Dataset for Digital Emulation of Cinematic Pro-Mist Filter Effects
Yingtie Lei, Zimeng Li, Chi-Man Pun +3
Pro-Mist filters are widely used in cinematography for their ability to create soft halation, lower contrast, and produce a distinctive, atmospheric style. These effects are diffic…
Elevating Medical Image Security: A Cryptographic Framework Integrating Hyperchaotic Map and GRU
Weixuan Li, Guang Yu, Quanjun Li +8
Chaotic systems play a key role in modern image encryption due to their sensitivity to initial conditions, ergodicity, and complex dynamics. However, many existing chaos-based encr…
TDADL-IE: A Deep Learning-Driven Cryptographic Architecture for Medical Image Security
Junhua Zhou, Quanjun Li, Weixuan Li +7
The rise of digital medical imaging, like MRI and CT, demands strong encryption to protect patient data in telemedicine and cloud storage. Chaotic systems are popular for image enc…
EEMS: Edge-Prompt Enhanced Medical Image Segmentation Based on Learnable Gating Mechanism
Han Xia, Quanjun Li, Qian Li +5
Medical image segmentation is vital for diagnosis, treatment planning, and disease monitoring but is challenged by complex factors like ambiguous edges and background noise. We int…
DTEA: Dynamic Topology Weaving and Instability-Driven Entropic Attenuation for Medical Image Segmentation
Weixuan Li, Quanjun Li, Guang Yu +5
In medical image segmentation, skip connections are used to merge global context and reduce the semantic gap between encoder and decoder. Current methods often struggle with limite…
PC-UNet: An Enforcing Poisson Statistics U-Net for Positron Emission Tomography Denoising
Yang Shi, Jingchao Wang, Liangsi Lu +9
Positron Emission Tomography (PET) is crucial in medicine, but its clinical use is limited due to high signal-to-noise ratio doses increasing radiation exposure. Lowering doses inc…
FS-RWKV: Leveraging Frequency Spatial-Aware RWKV for 3T-to-7T MRI Translation
Yingtie Lei, Zimeng Li, Chi-Man Pun +2
Ultra-high-field 7T MRI offers enhanced spatial resolution and tissue contrast that enables the detection of subtle pathological changes in neurological disorders. However, the lim…
SFormer: SNR-guided Transformer for Underwater Image Enhancement from the Frequency Domain
Xin Tian, Yingtie Lei, Xiujun Zhang +3
Recent learning-based underwater image enhancement (UIE) methods have advanced by incorporating physical priors into deep neural networks, particularly using the signal-to-noise ra…
CMAMRNet: A Contextual Mask-Aware Network Enhancing Mural Restoration Through Comprehensive Mask Guidance
Yingtie Lei, Fanghai Yi, Yihang Dong +5
Murals, as invaluable cultural artifacts, face continuous deterioration from environmental factors and human activities. Digital restoration of murals faces unique challenges due t…
Underwater Image Restoration via Polymorphic Large Kernel CNNs
Xiaojiao Guo, Yihang Dong, Xuhang Chen +4
Underwater Image Restoration (UIR) remains a challenging task in computer vision due to the complex degradation of images in underwater environments. While recent approaches have l…
High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer
Mingxian Li, Hao Sun, Yingtie Lei +5
Document images are often degraded by various stains, significantly impacting their readability and hindering downstream applications such as document digitization and analysis. Th…
Test-Time Intensity Consistency Adaptation for Shadow Detection
Leyi Zhu, Weihuang Liu, Xinyi Chen +4
Shadow detection is crucial for accurate scene understanding in computer vision, yet it is challenged by the diverse appearances of shadows caused by variations in illumination, ob…