11 citations · 33 across the 13 of their papers we have counts for
5 papers · 1 filter
Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach
Lingchen Sun, Rongyuan Wu, Zhiyuan Ma +3
Diffusion prior-based methods have shown impressive results in real-world image super-resolution (SR). However, most existing methods entangle pixel-level and semantic-level SR obj…
Adversarial Diffusion Compression for Real-World Image Super-Resolution
Bin Chen, Gehui Li, Rongyuan Wu +4
Real-world image super-resolution (Real-ISR) aims to reconstruct high-resolution images from low-resolution inputs degraded by complex, unknown processes. While many Stable Diffusi…
One-Step Effective Diffusion Network for Real-World Image Super-Resolution
Rongyuan Wu, Lingchen Sun, Zhiyuan Ma +1
The pre-trained text-to-image diffusion models have been increasingly employed to tackle the real-world image super-resolution (Real-ISR) problem due to their powerful generative i…
Dual Memory Networks: A Versatile Adaptation Approach for Vision-Language Models
Yabin Zhang, Wenjie Zhu, Hui Tang +3
With the emergence of pre-trained vision-language models like CLIP, how to adapt them to various downstream classification tasks has garnered significant attention in recent resear…
Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-Resolution
Lingchen Sun, Rongyuan Wu, Jie Liang +3
The generative priors of pre-trained latent diffusion models (DMs) have demonstrated great potential to enhance the visual quality of image super-resolution (SR) results. However,…