collaborators

8 papers

cs.CV2026

IQ-LUT: interpolated and quantized LUT for efficient image super-resolution

Yuxuan Zhang, Zhikai Dong, Xinning Chai +4

Lookup table (LUT) methods demonstrate considerable potential in accelerating image super-resolution inference. However, pursuing higher image quality through larger receptive fiel…

cs.CV2026

Joint Degradation-Aware Arbitrary-Scale Super-Resolution for Variable-Rate Extreme Image Compression

Xinning Chai, Zhengxue Cheng, Xin Li +2

Recent diffusion-based extreme image compression methods have demonstrated remarkable performance at ultra-low bitrates. However, most approaches require training separate diffusio…

cs.CV2025

OmniScaleSR: Unleashing Scale-Controlled Diffusion Prior for Faithful and Realistic Arbitrary-Scale Image Super-Resolution

Xinning Chai, Zhengxue Cheng, Yuhong Zhang +5

Arbitrary-scale super-resolution (ASSR) overcomes the limitation of traditional super-resolution (SR) methods that operate only at fixed scales (e.g., 4x), enabling a single model…

cs.CV2025

Semantic and Temporal Integration in Latent Diffusion Space for High-Fidelity Video Super-Resolution

Yiwen Wang, Xinning Chai, Yuhong Zhang +4

Recent advancements in video super-resolution (VSR) models have demonstrated impressive results in enhancing low-resolution videos. However, due to limitations in adequately contro…

eess.IV2025

NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results

Xin Li, Kun Yuan, Bingchen Li +110

This paper presents a review for the NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement. The challenge comprises two tracks: (i) Efficient Video Qualit…

cs.CV2025

Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-Resolution

Xinning Chai, Yao Zhang, Yuxuan Zhang +4

Convolutional neural networks (CNNs) have been widely used in efficient image super-resolution. However, for CNN-based methods, performance gains often require deeper networks and…