20 citations · 21 across the 9 of their papers we have counts for
12 papers
DriftSR: One-Step Real-World Image Super-Resolution via Distribution Drifting
Wei Zhu, Kai Zhang, Yu Zheng +4
One-step real-world image super-resolution (Real-ISR) offers efficient inference, but recovering realistic and perceptually rich details often relies on score distillation or adver…
ColorFM: An Optimization-to-Learning Framework for Color Transfer via Flow Matching
Yuhang He, Kai Zhang, Xiaoming Li +2
Color transfer aims to align the color distribution of a source image with that of a reference image while preserving structural and semantic consistency. However, existing methods…
OP4KSR: One-Step Patch-Free 4K Super-Resolution with Periodic Artifact Suppression
Chengyan Deng, Pengbin Yu, Zhentao Chen +6
Diffusion-based real-world image super-resolution (Real-ISR) has achieved remarkable perceptual quality; however, directly super-resolving images to 4K remains limited by extreme m…
Noise-Started One-Step Real-World Super-Resolution via LR-Conditioned SplitMeanFlow and GAN Refinement
Wei Zhu, Kai Zhang, Yu Zheng +3
Pre-trained text-to-image (T2I) diffusion models have shown strong potential for real-world image super-resolution (Real-ISR), owing to their noise-started generation process that…
From Zero to Detail: A Progressive Spectral Decoupling Paradigm for UHD Image Restoration with New Benchmark
Chen Zhao, Yunzhe Xu, Zhizhou Chen +5
Ultra-high-definition (UHD) image restoration poses unique challenges due to the high spatial resolution, diverse content, and fine-grained structures present in UHD images. To add…
MFSR: MeanFlow Distillation for One Step Real-World Image Super Resolution
Ruiqing Wang, Kai Zhang, Yuanzhi Zhu +3
Diffusion- and flow-based models have advanced Real-world Image Super-Resolution (Real-ISR), but their multi-step sampling makes inference slow and hard to deploy. One-step distill…
Joint Geometric and Trajectory Consistency Learning for One-Step Real-World Super-Resolution
Chengyan Deng, Zhangquan Chen, Li Yu +3
Diffusion-based Real-World Image Super-Resolution (Real-ISR) achieves impressive perceptual quality but suffers from high computational costs due to iterative sampling. While recen…
LUVE : Latent-Cascaded Ultra-High-Resolution Video Generation with Dual Frequency Experts
Chen Zhao, Jiawei Chen, Hongyu Li +6
Recent advances in video diffusion models have significantly improved visual quality, yet ultra-high-resolution (UHR) video generation remains a formidable challenge due to the com…
Reverse Convolution and Its Applications to Image Restoration
Xuhong Huang, Shiqi Liu, Kai Zhang +4
Convolution and transposed convolution are fundamental operators widely used in neural networks. However, transposed convolution (a.k.a. deconvolution) does not serve as a true inv…
Investigating Text Insulation and Attention Mechanisms for Complex Visual Text Generation
Ying Tai, Nikai Du, Rui Xie +5
In this paper, we present TextCrafter, a Complex Visual Text Generation (CVTG) framework inspired by selective visual attention in cognitive science, and introduce the "Text Insula…
STAR: Spatial-Temporal Augmentation with Text-to-Video Models for Real-World Video Super-Resolution
Rui Xie, Yinhong Liu, Penghao Zhou +7
Image diffusion models have been adapted for real-world video super-resolution to tackle over-smoothing issues in GAN-based methods. However, these models struggle to maintain temp…
MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution
Chengxing Xie, Xiaoming Zhang, Linze Li +4
Image super-resolution (SR) has significantly advanced through the adoption of Transformer architectures. However, conventional techniques aimed at enlarging the self-attention win…