8 papers
MeanSR: Restoration Trajectory Learning for One-Step Perceptual Super-Resolution
Axi Niu, Jiawei Kou, Kang Zhang +3
Diffusion-based super-resolution (SR) achieves strong perceptual quality but requires costly iterative denoising. Existing one-step distillation methods reduce inference time but d…
Coupled Continuous-Discrete Generation for Scene Text Image Super-Resolution
Axi Niu, Knag Zhang, Qingsen Yan +3
Scene text image super-resolution (STISR) aims to recover visually plausible appearance while preserving character semantics from degraded inputs. Existing STISR systems often rely…
FaithIR: Rethinking Infrared Image Super-Resolution from Perceptual Sharpness to Task Relevant Fidelity
Axi Niu, Zhenguo Wu, Kang Zhang +3
Infrared image super-resolution (IISR) is important for downstream tasks such as object detection and semantic segmentation. Existing IISR methods often produce artificial textures…
DualTSR: Unified Dual-Diffusion Transformer for Scene Text Image Super-Resolution
Axi Niu, Kang Zhang, Qingsen Yan +3
Scene Text Image Super-Resolution (STISR) aims to restore high-resolution details in low-resolution text images, which is crucial for both human readability and machine recognition…
Boosting HDR Image Reconstruction via Semantic Knowledge Transfer
Tao Hu, Longyao Wu, Wei Dong +5
Recovering High Dynamic Range (HDR) images from multiple Standard Dynamic Range (SDR) images become challenging when the SDR images exhibit noticeable degradation and missing conte…
Multi-Granularity Language-Guided Training for Multi-Object Tracking
Yuhao Li, Jiale Cao, Muzammal Naseer +4
Most existing multi-object tracking methods typically learn visual tracking features via maximizing dis-similarities of different instances and minimizing similarities of the same…