13 citations · 14 across the 23 of their papers we have counts for
28 papers
SCOPE-VSR: A Broad-Coverage Training Dataset for Video Super-Resolution
Gengyu Liao, Yechun Yong, Zheng Chen +4
Open-domain video super-resolution (VSR) requires temporally coherent details across widely varying scenes and subjects. However, existing training datasets are often semantically…
SPHQuant: Efficient extreme low bit weight quantization for Vision-Language Models
Kewei Zhang, Zheng Chen, Haotong Qin +1
Recent foundation models are moving toward native multimodal Vision-Language Models (VLMs), making VLMs a central form of next-generation foundation models. However, their large la…
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;…
LSGQuant: Layer-Sensitivity Guided Quantization for One-Step Diffusion Real-World Video Super-Resolution
Tianxing Wu, Zheng Chen, Cirou Xu +5
One-Step Diffusion Models have demonstrated promising capability and fast inference in video super-resolution (VSR) for real-world. Nevertheless, the substantial model size and hig…
BinaryDemoire: Moiré-Aware Binarization for Image Demoiréing
Zheng Chen, Zhi Yang, Xiaoyang Liu +5
Image demoiréing aims to remove structured moiré artifacts in recaptured imagery, where degradations are highly frequency-dependent and vary across scales and directions. While rec…
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…
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…
InfVSR: Toward Consistency-Driven Streaming Generative Video Super-Resolution
Ziqing Zhang, Kai Liu, Zheng Chen +5
Real-world videos often extend over thousands of frames. Existing generative video super-resolution (VSR) approaches, however, face two persistent challenges when processing long s…
Towards Redundancy Reduction in Diffusion Models for Efficient Video Super-Resolution
Jinpei Guo, Yifei Ji, Shengwei Wang +8
Diffusion models have recently shown promising results for video super-resolution (VSR). However, directly adapting generative diffusion models to VSR can result in redundancy, sin…
Steering One-Step Diffusion Model with Fidelity-Rich Decoder for Fast Image Compression
Zheng Chen, Mingde Zhou, Jinpei Guo +3
Diffusion-based image compression has demonstrated impressive perceptual performance. However, it suffers from two critical drawbacks: (1) excessive decoding latency due to multi-s…
QuantVSR: Low-Bit Post-Training Quantization for Real-World Video Super-Resolution
Bowen Chai, Zheng Chen, Libo Zhu +3
Diffusion models have shown superior performance in real-world video super-resolution (VSR). However, the slow processing speeds and heavy resource consumption of diffusion models…
StainPIDR: A Pathological Image Decouplingand Reconstruction Method for Stain Normalization Based on Color Vector Quantization and Structure Restaining
Zheng Chen
The color appearance of a pathological image is highly related to the imaging protocols, the proportion of different dyes, and the scanning devices. Computer-aided diagnostic syste…
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…
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…
DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution
Zheng Chen, Zichen Zou, Kewei Zhang +4
Diffusion models have demonstrated promising performance in real-world video super-resolution (VSR). However, the dozens of sampling steps they require, make inference extremely sl…
OSCAR: One-Step Diffusion Codec Across Multiple Bit-rates
Jinpei Guo, Yifei Ji, Zheng Chen +6
Pretrained latent diffusion models have shown strong potential for lossy image compression, owing to their powerful generative priors. Most existing diffusion-based methods reconst…
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…
CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-Resolution
Kai Liu, Dehui Wang, Zhiteng Li +5
Low-bit model quantization for image super-resolution (SR) is a longstanding task that is renowned for its surprising compression and acceleration ability. However, accuracy degrad…
BiMaCoSR: Binary One-Step Diffusion Model Leveraging Flexible Matrix Compression for Real Super-Resolution
Kai Liu, Kaicheng Yang, Zheng Chen +5
While super-resolution (SR) methods based on diffusion models (DM) have demonstrated inspiring performance, their deployment is impeded due to the heavy request of memory and compu…
Compression-Aware One-Step Diffusion Model for JPEG Artifact Removal
Jinpei Guo, Zheng Chen, Wenbo Li +2
Diffusion models have demonstrated remarkable success in image restoration tasks. However, their multi-step denoising process introduces significant computational overhead, limitin…
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…
Contrast: A Hybrid Architecture of Transformers and State Space Models for Low-Level Vision
Aman Urumbekov, Zheng Chen
Transformers have become increasingly popular for image super-resolution (SR) tasks due to their strong global context modeling capabilities. However, their quadratic computational…