Task-driven image restoration aims to improve both image quality and downstream task performance. However, existing methods predominantly focus on single degradation type and struggle to handle the diverse degradations encountered in real-world scenarios. Different degradations impose distinct restoration demands, and...
Yan-Jie Tu, Qing-Sen Yan, Axi Niu et al.· 0 citations
DualTSR is presented, a unified framework that formulates STISR as coupled continuous-discrete generation and achieves the best FID, LPIPS, ACC, and NED among the compared methods at both X2 and X4.
Axi Niu, Knag Zhang, Qing-Sen Yan et al.· 0 citations
A novel Dual-Prior Collaborative Network (DPC-Net) is proposed, which achieves high-quality restoration by jointly exploiting degradation-semantic coupled priors and low-level visual priors and propagates coupled representations to the decoder.
Zhaokun He, Kangbiao Shi, Axi Niu et al.· 0 citations
TGFusion is proposed, a text-guided latent-space flow matching framework that unifies degradation suppression and cross-modal fusion and achieves superior or competitive performance in perceptual quality, image naturalness, structural-detail preservation, and infrared-saliency retention, while remaining robust across d...
Axi Niu, Jiehua Li, Kang Zhang et al.· 0 citations
This work proposes MeanSR, a one-step perceptual SR method that learns an LR-conditioned average velocity field to directly capture the finite-time transition from degraded or noisy inputs to plausible HR outputs and introduces a Stage-Aware Temporal Sampling strategy to improve trajectory learning.
Axi Niu, Jiawei Kou, Kang Zhang et al.· 0 citations
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