13 citations · 14 across the 22 of their papers we have counts for
25 papers
BC-IHV: Conditioning the Color Space for Stable Rectified-Flow Low-Light Enhancement
Yi Ai, Zheng Chen, Yuanhao Cai +2
Low-light image enhancement (LLIE) must correct ambiguous exposure without overwriting structure already supported by the input. Generative transport can model exposure ambiguity;…
Bricker to BRACE: A Bracket Exposure RAW Dataset and Restoration Model for Flicker-Banding
Zihan Zhou, Libo Zhu, Jue Gong +4
Flicker-banding (FB), arises from temporal aliasing between a camera's rolling shutter and a display's brightness modulation, degrading screen-captured image readability with color…
QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks
Haotong Qin, Xudong Ma, Xianglong Liu +4
Low-bit quantization is widely used to compress super-resolution (SR) models and reduce storage and computation costs for deployment on resource-limited devices. However, when SR m…
VDFP: Video Deflickering with Flicker-banding Priors
Zhiyi Zhou, Libo Zhu, Zihan Zhou +2
Capturing digital screens with smartphones frequently induces severe banding due to hardware synchronization mismatches. Existing video restoration methods struggle with these stru…
Accelerating Rectified Flow Models via Trajectory-Aware Caching
Xiao Liu, Kai Liu, Naiyang Guan +5
Diffusion and rectified flow (RF) models generate high-fidelity images and videos, but their iterative velocity-field evaluations are computationally expensive. Existing caching me…
DVFace: Spatio-Temporal Dual-Prior Diffusion for Video Face Restoration
Zheng Chen, Bowen Chai, Rongjun Gao +7
Video face restoration aims to enhance degraded face videos into high-quality results with realistic facial details, stable identity, and temporal coherence. Recent diffusion-based…
The Fourth Challenge on Image Super-Resolution (4) at NTIRE 2026: Benchmark Results and Method Overview
Zheng Chen, Kai Liu, Jingkai Wang +150
This paper presents the NTIRE 2026 image super-resolution (4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026. The challenge aims to r…
LCUDiff: Latent Capacity Upgrade Diffusion for Faithful Human Body Restoration
Jue Gong, Zihan Zhou, Jingkai Wang +4
Existing methods for restoring degraded human-centric images often struggle with insufficient fidelity, particularly in human body restoration (HBR). Recent diffusion-based restora…
Light Up Your Face: A Physically Consistent Dataset and Diffusion Model for Face Fill-Light Enhancement
Jue Gong, Zihan Zhou, Jingkai Wang +3
Face fill-light enhancement (FFE) brightens underexposed faces by adding virtual fill light while keeping the original scene illumination and background unchanged. Most face religh…
Combined Flicker-banding and Moire Removal for Screen-Captured Images
Libo Zhu, Zihan Zhou, Zhiyi Zhou +5
Capturing display screens with mobile devices has become increasingly common, yet the resulting images often suffer from severe degradations caused by the coexistence of moiré patt…
VEQ: Modality-Adaptive Quantization for MoE Vision-Language Models
Guangshuo Qin, Zhiteng Li, Zheng Chen +3
Mixture-of-Experts(MoE) Vision-Language Models (VLMs) offer remarkable performance but incur prohibitive memory and computational costs, making compression essential. Post-Training…
RIFLE: Removal of Image Flicker-Banding via Latent Diffusion Enhancement
Libo Zhu, Zihan Zhou, Xiaoyang Liu +4
Capturing screens is now routine in our everyday lives. But the photographs of emissive displays are often influenced by the flicker-banding (FB), which is alternating bright%u2013…
QuantDemoire: Quantization with Outlier Aware for Image Demoiréing
Zheng Chen, Kewei Zhang, Xiaoyang Liu +4
Demoiréing aims to remove moiré artifacts that often occur in images. While recent deep learning-based methods have achieved promising results, they typically require substantial c…
FideDiff: Efficient Diffusion Model for High-Fidelity Image Motion Deblurring
Xiaoyang Liu, Zhengyan Zhou, Zihang Xu +3
Recent advancements in image motion deblurring, driven by CNNs and transformers, have made significant progress. Large-scale pre-trained diffusion models, which are rich in real-wo…
OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions
Yuanhao Cai, He Zhang, Xi Chen +11
Existing feedforward subject-driven video customization methods mainly study single-subject scenarios due to the difficulty of constructing multi-subject training data pairs. Anoth…
A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarking
Zixiang Zhao, Haowen Bai, Bingxin Ke +5
The real world is dynamic, yet most image fusion methods process static frames independently, ignoring temporal correlations in videos and leading to flickering and temporal incons…
HAODiff: Human-Aware One-Step Diffusion via Dual-Prompt Guidance
Jue Gong, Tingyu Yang, Jingkai Wang +5
Human-centered images often suffer from severe generic degradation during transmission and are prone to human motion blur (HMB), making restoration challenging. Existing research l…
Freqformer: Image-Demoiréing Transformer via Effective Frequency Decomposition
Xiaoyang Liu, Bolin Qiu, Zheng Chen +5
Image demoiréing remains a challenging task due to the complex interplay between texture corruption and color distortions caused by moiré patterns. Existing methods, especially tho…
DVD-Quant: Data-free Video Diffusion Transformers Quantization
Zhiteng Li, Hanxuan Li, Junyi Wu +6
Diffusion Transformers (DiTs) have emerged as the state-of-the-art architecture for video generation, yet their computational and memory demands hinder practical deployment. While…
HonestFace: Towards Honest Face Restoration with One-Step Diffusion Model
Jingkai Wang, Wu Miao, Jue Gong +5
Face restoration has achieved significant advancements through the years of development. However, maintaining high fidelity and authenticity while avoiding artifacts remains challe…
QuantCache: Adaptive Importance-Guided Quantization with Hierarchical Latent and Layer Caching for Video Generation
Junyi Wu, Zhiteng Li, Zheng Hui +3
Recently, Diffusion Transformers (DiTs) have emerged as a dominant architecture in video generation, surpassing U-Net-based models in terms of performance. However, the enhanced ca…
One-Step Diffusion Model for Image Motion-Deblurring
Xiaoyang Liu, Yuquan Wang, Zheng Chen +4
Currently, methods for single-image deblurring based on CNNs and transformers have demonstrated promising performance. However, these methods often suffer from perceptual limitatio…
Human Body Restoration with One-Step Diffusion Model and A New Benchmark
Jue Gong, Jingkai Wang, Zheng Chen +4
Human body restoration, as a specific application of image restoration, is widely applied in practice and plays a vital role across diverse fields. However, thorough research remai…
OSDFace: One-Step Diffusion Model for Face Restoration
Jingkai Wang, Jue Gong, Lin Zhang +6
Diffusion models have demonstrated impressive performance in face restoration. Yet, their multi-step inference process remains computationally intensive, limiting their applicabili…