72 citations · 88 across the 4 of their papers we have counts for
5 papers
LPNSR: Learnable Noise Prediction for Diffusion-Based Image Super-Resolution
Shuwei Huang, Shizhuo Liu, Zijun Wei
Diffusion-based image super-resolution (SR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) observations. A key property of diffusion models is that, once…
Enhancing Noise Resilience in Face Clustering via Sparse Differential Transformer
Dafeng Zhang, Yongqi Song, Shizhuo Liu
The method used to measure relationships between face embeddings plays a crucial role in determining the performance of face clustering. Existing methods employ the Jaccard similar…
From Cradle to Cane: A Two-Pass Framework for High-Fidelity Lifespan Face Aging
Tao Liu, Dafeng Zhang, Gengchen Li +7
Face aging has become a crucial task in computer vision, with applications ranging from entertainment to healthcare. However, existing methods struggle with achieving a realistic a…
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
Ren Yang, Radu Timofte, Xin Li +49
This paper reviews the Challenge on Super-Resolution of Compressed Image and Video at AIM 2022. This challenge includes two tracks. Track 1 aims at the super-resolution of compress…
SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution
Dafeng Zhang, Feiyu Huang, Shizhuo Liu +2
Transformer-based methods have achieved impressive image restoration performance due to their capacities to model long-range dependency compared to CNN-based methods. However, adva…