4 citations · 6 across the 22 of their papers we have counts for
16 papers · 1 filter
Predicting Functions, Not Features: KANs with Function-Space Joint-Embedding Predictive Learning for Medical Image Segmentation
Yungeng Liu, Xuanzi Fang, Yuge Zhang +3
Kolmogorov--Arnold Networks (KANs) introduce explicit functional representations by parameterizing each network edge as a learnable univariate function. However, existing KAN-based…
SegDem: Segmentation helps Demosaicing
Ping Chen, Xiangming Wang, Yongyong Chen +5
Image demosaicing reconstructs a full-color image from incomplete color measurements produced by a sensor covered with a color filter array (CFA). Most existing methods formulate d…
The Second LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results
Xiang Chen, Hao Li, Jiangxin Dong +89
This paper presents a review of the second LoViF Challenge on Real-World All-in-One Image Restoration. The challenge aims to advance unified image restoration under diverse real-wo…
Decoupling Language Guidance from Backbones for Text-Guided Medical Segmentation
Yungeng Liu, Xuanzi Fang, Haijin Zeng +2
Text-guided medical image segmentation leverages clinical semantics to improve lesion delineation, yet many existing models bind cross-modal fusion, supervision, and decoder design…
Differential Unfolding: Efficient Unfolding Reconstruction for Video Snapshot Compressive Imaging
Muyuan Zhang, Jiancheng Zhang, Haijin Zeng +1
While Deep Unfolding Networks (DUNs) dominate video Snapshot Compressive Imaging (SCI), they remain constrained by a uniform design philosophy. Existing methods repeatedly stack hi…
Deep LoRA-Unfolding Networks for Image Restoration
Xiangming Wang, Haijin Zeng, Benteng Sun +4
Deep unfolding networks (DUNs), combining conventional iterative optimization algorithms and deep neural networks into a multi-stage framework, have achieved remarkable accomplishm…
HSI-VAR: Rethinking Hyperspectral Restoration through Spatial-Spectral Visual Autoregression
Xiangming Wang, Benteng Sun, Yungeng Liu +4
Hyperspectral images (HSIs) capture richer spatial-spectral information beyond RGB, yet real-world HSIs often suffer from a composite mix of degradations, such as noise, blur, and…
SlowFast-SCI: Slow-Fast Deep Unfolding Learning for Spectral Compressive Imaging
Haijin Zeng, Xuan Lu, Jiezhang Cao +8
Humans learn in two complementary ways: a slow, cumulative process that builds broad, general knowledge, and a fast, on-the-fly process that captures specific experiences. Existing…
Lightweight Quad Bayer HybridEVS Demosaicing via State Space Augmented Cross-Attention
Shiyang Zhou, Haijin Zeng, Yunfan Lu +3
Event cameras like the Hybrid Event-based Vision Sensor (HybridEVS) camera capture brightness changes as asynchronous "events" instead of frames, offering advanced application on m…
Vision-Language Gradient Descent-driven All-in-One Deep Unfolding Networks
Haijin Zeng, Xiangming Wang, Yongyong Chen +2
Dynamic image degradations, including noise, blur and lighting inconsistencies, pose significant challenges in image restoration, often due to sensor limitations or adverse environ…
Binarized Mamba-Transformer for Lightweight Quad Bayer HybridEVS Demosaicing
Shiyang Zhou, Haijin Zeng, Yunfan Lu +5
Quad Bayer demosaicing is the central challenge for enabling the widespread application of Hybrid Event-based Vision Sensors (HybridEVS). Although existing learning-based methods t…
OTLRM: Orthogonal Learning-based Low-Rank Metric for Multi-Dimensional Inverse Problems
Xiangming Wang, Haijin Zeng, Jiaoyang Chen +3
In real-world scenarios, complex data such as multispectral images and multi-frame videos inherently exhibit robust low-rank property. This property is vital for multi-dimensional…
MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging
Zhenghao Pan, Haijin Zeng, Jiezhang Cao +3
Color video snapshot compressive imaging (SCI) employs computational imaging techniques to capture multiple sequential video frames in a single Bayer-patterned measurement. With th…
Cross-Scan Mamba with Masked Training for Robust Spectral Imaging
Wenzhe Tian, Haijin Zeng, Yin-Ping Zhao +3
Snapshot Compressive Imaging (SCI) enables fast spectral imaging but requires effective decoding algorithms for hyperspectral image (HSI) reconstruction from compressed measurement…
LLEMamba: Low-Light Enhancement via Relighting-Guided Mamba with Deep Unfolding Network
Xuanqi Zhang, Haijin Zeng, Jinwang Pan +2
Transformer-based low-light enhancement methods have yielded promising performance by effectively capturing long-range dependencies in a global context. However, their elevated com…
DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral Diffusion Model
Zhenghao Pan, Haijin Zeng, Jiezhang Cao +2
This paper endeavors to advance the precision of snapshot compressive imaging (SCI) reconstruction for multispectral image (MSI). To achieve this, we integrate the advantageous att…