19 citations · 36 across the 26 of their papers we have counts for
27 papers
TaskIR: Task-Driven Image Restoration via Degradation Adaptation and Task Feedback
Yanjie Tu, Qingsen Yan, Axi Niu +4
Task-driven image restoration aims to improve both image quality and downstream task performance. However, existing methods predominantly focus on single degradation type and strug…
DPC-Net: Dual-Prior Collaborative Network for All-in-One Image Restoration
Zhaokun He, Kangbiao Shi, Axi Niu +4
All-in-One Image Restoration (AiOIR) aims to handle diverse degradations within a unified model. However, existing methods often overlook image semantics in degradation modeling an…
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…
DVANet: Degradation-aware Visual-prior Alignment Network for Image Restoration
Yanjie Tu, Qingsen Yan, Axi Niu +3
All-in-One image restoration aims to develop a unified restoration framework for handling diverse degradation types. Existing end-to-end methods usually regard the restoration proc…
EAPFusion: Intrinsic Evolving Auxiliary Prior Guidance for Infrared and Visible Image Fusion
Zhenyu Sun, Luobin Zhang, Axi Niu +2
Infrared-visible image fusion aims to create an information-rich fused image by integrating the complementary thermal saliency from infrared sensing and fine textures from visible…
IConFace: Fine-Grained Identity Conditioning for Reference-Aware Face Restoration
Axi Niu, Jinyang Zhang, Senyan Qing
Severe face degradation can remove person-specific evidence, making restoration underdetermined. A generative prior may recover a sharp, plausible face yet miss localized traits th…
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…
Redefining Quality Criteria and Distance-Aware Score Modeling for Image Editing Assessment
Xinjie Zhang, Qiang Li, Xiaowen Ma +3
Recent advances in image editing have heightened the need for reliable Image Editing Quality Assessment (IEQA). Unlike traditional methods, IEQA requires complex reasoning over mul…
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…
TPGDiff: Hierarchical Triple-Prior Guided Diffusion for Image Restoration
Yanjie Tu, Qingsen Yan, Axi Niu +1
All-in-one image restoration aims to address diverse degradation types using a single unified model. Existing methods typically rely on degradation priors to guide restoration, yet…
Model-Guided Dual-Role Alignment for High-Fidelity Open-Domain Video-to-Audio Generation
Kang Zhang, Trung X. Pham, Suyeon Lee +3
We present MGAudio, a novel flow-based framework for open-domain video-to-audio generation, which introduces model-guided dual-role alignment as a central design principle. Unlike…
Towards Understanding Dual BN In Hybrid Adversarial Training
Chenshuang Zhang, Chaoning Zhang, Kang Zhang +3
There is a growing concern about applying batch normalization (BN) in adversarial training (AT), especially when the model is trained on both adversarial samples and clean samples…
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution
Axi Niu, Kang Zhang, Joshua Tian Jin Tee +5
It is well known the adversarial optimization of GAN-based image super-resolution (SR) methods makes the preceding SR model generate unpleasant and undesirable artifacts, leading t…
Multiple Object Tracking based on Occlusion-Aware Embedding Consistency Learning
Yaoqi Hu, Axi Niu, Yu Zhu +3
The Joint Detection and Embedding (JDE) framework has achieved remarkable progress for multiple object tracking. Existing methods often employ extracted embeddings to re-establish…
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution
Axi Niu, Pham Xuan Trung, Kang Zhang +4
Diffusion models have gained significant popularity in the field of image-to-image translation. Previous efforts applying diffusion models to image super-resolution (SR) have demon…
Learning from Multi-Perception Features for Real-Word Image Super-resolution
Axi Niu, Kang Zhang, Trung X. Pham +4
Currently, there are two popular approaches for addressing real-world image super-resolution problems: degradation-estimation-based and blind-based methods. However, degradation-es…
GRAN: Ghost Residual Attention Network for Single Image Super Resolution
Axi Niu, Pei Wang, Yu Zhu +3
Recently, many works have designed wider and deeper networks to achieve higher image super-resolution performance. Despite their outstanding performance, they still suffer from hig…
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution
Axi Niu, Kang Zhang, Trung X. Pham +4
Diffusion probabilistic models (DPM) have been widely adopted in image-to-image translation to generate high-quality images. Prior attempts at applying the DPM to image super-resol…
Self-Supervised Visual Representation Learning via Residual Momentum
Trung X. Pham, Axi Niu, Zhang Kang +5
Self-supervised learning (SSL) approaches have shown promising capabilities in learning the representation from unlabeled data. Amongst them, momentum-based frameworks have attract…
On the Pros and Cons of Momentum Encoder in Self-Supervised Visual Representation Learning
Trung Pham, Chaoning Zhang, Axi Niu +2
Exponential Moving Average (EMA or momentum) is widely used in modern self-supervised learning (SSL) approaches, such as MoCo, for enhancing performance. We demonstrate that such m…
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness
Chaoning Zhang, Kang Zhang, Chenshuang Zhang +4
Adversarial training (AT) for robust representation learning and self-supervised learning (SSL) for unsupervised representation learning are two active research fields. Integrating…