12 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…
Unleashing the Power of Text: Text-Guided Flow Matching for Image Fusion under Complex Degradations
Axi Niu, Jieheng Li, Kang Zhang +3
Infrared-visible image fusion under realistic degradation scenarios is a challenging task, as degradations not only cause a loss of reliable modality-specific information in observ…
The Second Challenge on Real-World Face Restoration at NTIRE 2026: Methods and Results
Jingkai Wang, Jue Gong, Zheng Chen +50
This paper provides a review of the NTIRE 2026 challenge on real-world face restoration, highlighting the proposed solutions and the resulting outcomes. The challenge focuses on ge…
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…