activity
20232026
most citedReti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model

10 citations · 16 across the 22 of their papers we have counts for

collaborators

23 papers

cs.CV2026

UnfoldCRF: Structured Mask Refinement with Image-Conditioned Latent Regions

Chunming He, Rihan Zhang, Lei Xu +5

Learned mask refiners improve segmentation accuracy, but it is hard to tell how much of the improvement comes from explicit structure rather than from extra capacity, and whether i…

cs.CV2026

PRISM: Rethinking Atmospheric Scattering Reconstruction as a Unified Understanding and Restoration Model for Real-world Dehazing

Chengyu Fang, Chunming He, Yuelin Zhang +6

Real-world image dehazing (RID) aims to remove haze-induced degradation from real scenes. This task remains challenging due to non-uniform haze distribution, spatially varying colo…

cs.CV2026

RIDE: Retinex-Informed Decoupling for Exposing Concealed Objects

Chunming He, Rihan Zhang, Dingming Zhang +5

Concealed Object Segmentation (COS) encompasses a family of dense-prediction tasks, including camouflaged object detection, polyp segmentation, transparent object detection, and in…

cs.RO2026

A Vision-Language-Action Model for Adaptive Ultrasound-Guided Needle Insertion and Needle Tracking

Yuelin Zhang, Qingpeng Ding, Longxiang Tang +2

Ultrasound (US)-guided needle insertion is a critical yet challenging procedure due to dynamic imaging conditions and difficulties in needle visualization. Many methods have been p…

cs.CV2026

Beyond Ground-Truth: Leveraging Image Quality Priors for Real-World Image Restoration

Fengyang Xiao, Peng Hu, Lei Xu +7

Real-world image restoration aims to restore high-quality (HQ) images from degraded low-quality (LQ) inputs captured under uncontrolled conditions. Existing methods typically depen…

cs.CV2026

Photon: Speedup Volume Understanding with Efficient Multimodal Large Language Models

Chengyu Fang, Heng Guo, Zheng Jiang +3

Multimodal large language models are promising for clinical visual question answering tasks, but scaling to 3D imaging is hindered by high computational costs. Prior methods often…

cs.CV2025

Taming Preference Mode Collapse via Directional Decoupling Alignment in Diffusion Reinforcement Learning

Chubin Chen, Sujie Hu, Jiashu Zhu +8

Recent studies have demonstrated significant progress in aligning text-to-image diffusion models with human preference via Reinforcement Learning from Human Feedback. However, whil…

cs.CV2025

UnfoldLDM: Degradation-Aware Unfolding with Iterative Latent Diffusion Priors for Blind Image Restoration

Chunming He, Rihan Zhang, Zheng Chen +6

Deep unfolding networks (DUNs) combine the interpretability of model-based methods with the learning ability of deep networks, yet remain limited for blind image restoration (BIR).…

cs.CV2025

M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervision

Che Liu, Zheng Jiang, Chengyu Fang +5

Medical image retrieval is essential for clinical decision-making and translational research, relying on discriminative visual representations. Yet, current methods remain fragment…

cs.CV2025

Reversible Unfolding Network for Concealed Visual Perception with Generative Refinement

Chunming He, Fengyang Xiao, Rihan Zhang +3

Existing methods for concealed visual perception (CVP) often leverage reversible strategies to decrease uncertainty, yet these are typically confined to the mask domain, leaving th…

cs.CV2025

Exploring Fourier Prior and Event Collaboration for Low-Light Image Enhancement

Chunyan She, Fujun Han, Chengyu Fang +2

The event camera, benefiting from its high dynamic range and low latency, provides performance gain for low-light image enhancement. Unlike frame-based cameras, it records intensit…

cs.CV2025

Segment Concealed Objects with Incomplete Supervision

Chunming He, Kai Li, Yachao Zhang +8

Incompletely-Supervised Concealed Object Segmentation (ISCOS) involves segmenting objects that seamlessly blend into their surrounding environments, utilizing incompletely annotate…

cs.CV2025

Uncertainty-Masked Bernoulli Diffusion for Camouflaged Object Detection Refinement

Yuqi Shen, Fengyang Xiao, Sujie Hu +5

Camouflaged Object Detection (COD) presents inherent challenges due to the subtle visual differences between targets and their backgrounds. While existing methods have made notable…

cs.CV2025

UnfoldIR: Rethinking Deep Unfolding Network in Illumination Degradation Image Restoration

Chunming He, Rihan Zhang, Fengyang Xiao +4

Deep unfolding networks (DUNs) are widely employed in illumination degradation image restoration (IDIR) to merge the interpretability of model-based approaches with the generalizat…

cs.CV2025★ 1 cited

Integrating Extra Modality Helps Segmentor Find Camouflaged Objects Well

Chengyu Fang, Chunming He, Longxiang Tang +6

Camouflaged Object Segmentation (COS) remains challenging because camouflaged objects exhibit only subtle visual differences from their backgrounds and single-modality RGB methods…

cs.CV2024

InstantSwap: Fast Customized Concept Swapping across Sharp Shape Differences

Chenyang Zhu, Kai Li, Yue Ma +5

Recent advances in Customized Concept Swapping (CCS) enable a text-to-image model to swap a concept in the source image with a customized target concept. However, the existing meth…

cs.CV2024★ 2 cited

A Survey of Camouflaged Object Detection and Beyond

Fengyang Xiao, Sujie Hu, Yuqi Shen +6

Camouflaged Object Detection (COD) refers to the task of identifying and segmenting objects that blend seamlessly into their surroundings, posing a significant challenge for comput…

cs.CV2024★ 1 cited

Diffusion Models in Low-Level Vision: A Survey

Chunming He, Yuqi Shen, Chengyu Fang +6

Deep generative models have garnered significant attention in low-level vision tasks due to their generative capabilities. Among them, diffusion model-based solutions, characterize…

cs.CV2024

Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Network

Chengyu Fang, Chunming He, Fengyang Xiao +5

Real-world Image Dehazing (RID) aims to alleviate haze-induced degradation in real-world settings. This task remains challenging due to the complexities in accurately modeling real…

cs.CV2024

Motion-Guided Dual-Camera Tracker for Endoscope Tracking and Motion Analysis in a Mechanical Gastric Simulator

Yuelin Zhang, Kim Yan, Chun Ping Lam +5

Flexible endoscope motion tracking and analysis in mechanical simulators have proven useful for endoscopy training. Common motion tracking methods based on electromagnetic tracker…

cs.CV2024★ 1 cited

A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive Learning

Yuelin Zhang, Pengyu Zheng, Wanquan Yan +2

Defocus blur is a persistent problem in microscope imaging that poses harm to pathology interpretation and medical intervention in cell microscopy and microscope surgery. To addres…

cs.CV2023★ 10 cited

Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model

Chunming He, Chengyu Fang, Yulun Zhang +6

Illumination degradation image restoration (IDIR) techniques aim to improve the visibility of degraded images and mitigate the adverse effects of deteriorated illumination. Among t…

cs.CV2023★ 1 cited

Flow-Guided Controllable Line Drawing Generation

Chengyu Fang, Xianfeng Han

In this paper, we investigate the problem of automatically controllable artistic character line drawing generation from photographs by proposing a Vector Flow Aware and Line Contro…