Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution

17 April 2024

Papers citing "Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution"

6 / 6 papers shown

Title
$NTIRE 2025 Challenge on Image Super-Resolution ($\times$4): Methods and Results$ NTIRE 2025 Challenge on Image Super-Resolution ( $\times$ 4): Methods and Results Zheng Chen Kai Liu Jue Gong J. Wang Lei Sun ... Prashant Patil Santosh Kumar Vipparthi Subrahmanyam Murala Bilel Benjdira Anas M. Ali SupR 50 0 0 20 Apr 2025
The Tenth NTIRE 2025 Image Denoising Challenge Report Lei-huan Sun Hang Guo Bin Ren Luc Van Gool Radu Timofte ... Kun Li Shengeng Tang Y. Zhang Weirun Zhou Haoxuan Lu 3DGS 41 7 0 16 Apr 2025
AdaptSR: Low-Rank Adaptation for Efficient and Scalable Real-World Super-Resolution Cansu Korkmaz Nancy Mehta Radu Timofte 62 0 0 10 Mar 2025
$NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results$ NTIRE 2024 Challenge on Image Super-Resolution ( $\times$ 4): Methods and Results Zheng Chen Zongwei Wu Eduard Zamfir Kai Zhang Yulun Zhang ... Yan Luo Yanyan Wei Asif Hussain Khan C. Micheloni N. Martinel SupR 28 32 0 15 Apr 2024
PASTA: Towards Flexible and Efficient HDR Imaging Via Progressively Aggregated Spatio-Temporal Alignment Xiaoning Liu Ao Li Zongwei Wu Yapeng Du Le Zhang Yulun Zhang Radu Timofte Ce Zhu 23 2 0 15 Mar 2024
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions Wenhai Wang Enze Xie Xiang Li Deng-Ping Fan Kaitao Song Ding Liang Tong Lu Ping Luo Ling Shao ViT 263 3,538 0 24 Feb 2021