Jinhua Hao

Orcid: 0000-0003-4571-2063

According to our database1, Jinhua Hao authored at least 18 papers between 2024 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
Coloring the Noise: Adversarial Sobolev Alignment for Faithful Image Super Resolution.
CoRR, May, 2026

WebCompass: Towards Multimodal Web Coding Evaluation for Code Language Models.
CoRR, April, 2026

KAT-Coder-V2 Technical Report.
CoRR, March, 2026

Tuning Real-World Image Restoration at Inference: A Test-Time Scaling Paradigm for Flow Matching Models.
CoRR, March, 2026

ShiftLUT: Spatial Shift Enhanced Look-Up Tables for Efficient Image Restoration.
CoRR, March, 2026

WMFU-Net:Multi-scale Wavelet Mamba network and Adaptive fusion for retinal vessel segmentation.
Biomed. Signal Process. Control., 2026

2025
InstantViR: Real-Time Video Inverse Problem Solver with Distilled Diffusion Prior.
CoRR, November, 2025

HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement.
CoRR, April, 2025

Visual Autoregressive Modeling for Image Super-Resolution.
Proceedings of the Forty-second International Conference on Machine Learning, 2025

Plug-and-Play Tri-Branch Invertible Block for Image Rescaling.
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024
NTIRE 2024 Challenge on Image Super-Resolution (⨉4): Methods and Results.
CoRR, 2024

CasSR: Activating Image Power for Real-World Image Super-Resolution.
CoRR, 2024

XPSR: Cross-Modal Priors for Diffusion-Based Image Super-Resolution.
Proceedings of the Computer Vision - ECCV 2024, 2024

OAPT: Offset-Aware Partition Transformer for Double JPEG Artifacts Removal.
Proceedings of the Computer Vision - ECCV 2024, 2024

CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

PTM-VQA: Efficient Video Quality Assessment Leveraging Diverse PreTrained Models from the Wild.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024




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